WorldWideScience

Sample records for wind speed forecast

  1. Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

    Directory of Open Access Journals (Sweden)

    Niya Chen

    2013-01-01

    Full Text Available Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

  2. Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging

    National Research Council Canada - National Science Library

    Sloughter, J. M; Gneiting, Tilmann; Raftery, Adrian E

    2008-01-01

    Probabilistic forecasts of wind speed are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating...

  3. Short-Term Wind Speed Forecasting for Power System Operations

    KAUST Repository

    Zhu, Xinxin

    2012-04-01

    The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.

  4. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Guoqiang Sun

    2014-01-01

    Full Text Available With the development of wind power technology, the security of the power system, power quality, and stable operation will meet new challenges. So, in this paper, we propose a recently developed machine learning technique, relevance vector machine (RVM, for day-ahead wind speed forecasting. We combine Gaussian kernel function and polynomial kernel function to get mixed kernel for RVM. Then, RVM is compared with back propagation neural network (BP and support vector machine (SVM for wind speed forecasting in four seasons in precision and velocity; the forecast results demonstrate that the proposed method is reasonable and effective.

  5. Interval forecasts of a novelty hybrid model for wind speeds

    Directory of Open Access Journals (Sweden)

    Shanshan Qin

    2015-11-01

    Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.

  6. Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

    OpenAIRE

    Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang

    2015-01-01

    The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...

  7. Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Chuanan Yao

    2013-01-01

    Full Text Available Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN and loose wavelet neural network (LWNN in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

  8. Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques

    Directory of Open Access Journals (Sweden)

    Diksha Kaur

    2015-01-01

    Full Text Available The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT along with the Auto Regressive Moving Average (ARMA is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE. A simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ARMA and Artificial Neural Network (ANN-Ensemble Kalman Filter (EnKF hybrid technique to verify the effectiveness of the proposed hybrid method. Results of the proposed hybrid show significant improvements in the forecasting error.

  9. Day-ahead wind speed forecasting using f-ARIMA models

    International Nuclear Information System (INIS)

    Kavasseri, Rajesh G.; Seetharaman, Krithika

    2009-01-01

    With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. (author)

  10. Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

    KAUST Repository

    Zhu, Xinxin

    2014-09-01

    Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.

  11. Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm

    International Nuclear Information System (INIS)

    Xiao, Liye; Qian, Feng; Shao, Wei

    2017-01-01

    Highlights: • Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting. • Improve the accuracy of multi-step wind speed forecasting. • Modify bat algorithm with CG to improve optimized performance. - Abstract: As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting.

  12. Potential of Offshore Wind Energy and Extreme Wind Speed Forecasting on the West Coast of Taiwan

    Directory of Open Access Journals (Sweden)

    Pei-Chi Chang

    2015-02-01

    Full Text Available It is of great importance and urgency for Taiwan to develop offshore wind power. However, relevant data on offshore wind energy resources are limited. This study imported wind speeds measured by a tidal station and a buoy into the software WAsP to estimate the high-altitude wind speeds in the two areas. A light detection and ranging (Lidar system was set up near the tidal station and buoy. High-altitude wind speeds measured by the Lidar system were compared with the WAsP-estimated values, and it was discovered that the two data sets were consistent. Then, long-term wind speed data observed by buoys and tidal stations at various locations were imported into WAsP to forecast wind speeds at heights of 55–200 m on the west coast of Taiwan. The software WAsP Engineering was used to analyze the extreme wind speeds in the same areas. The results show that wind speeds at 100 m are approximately 9.32–11.24 m/s, which means that the coastal areas of west Taiwan are rich in wind energy resources. When a long-term 10-min average wind speed is used, the extreme wind speed on the west coast is estimated to be between 36.4 and 55.3 m/s.

  13. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area

    OpenAIRE

    Guo, Zhenhai; Dong, Yao; Wang, Jianzhou; Lu, Haiyan

    2010-01-01

    Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the fir...

  14. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area

    Directory of Open Access Journals (Sweden)

    Zhenhai Guo

    2010-01-01

    Full Text Available Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM; the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China.

  15. On multivariate imputation and forecasting of decadal wind speed missing data.

    Science.gov (United States)

    Wesonga, Ronald

    2015-01-01

    This paper demonstrates the application of multiple imputations by chained equations and time series forecasting of wind speed data. The study was motivated by the high prevalence of missing wind speed historic data. Findings based on the fully conditional specification under multiple imputations by chained equations, provided reliable wind speed missing data imputations. Further, the forecasting model shows, the smoothing parameter, alpha (0.014) close to zero, confirming that recent past observations are more suitable for use to forecast wind speeds. The maximum decadal wind speed for Entebbe International Airport was estimated to be 17.6 metres per second at a 0.05 level of significance with a bound on the error of estimation of 10.8 metres per second. The large bound on the error of estimations confirms the dynamic tendencies of wind speed at the airport under study.

  16. Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Ying-Yi Hong

    2013-11-01

    Full Text Available Operation of wind power generation in a large farm is quite challenging in a smart grid owing to uncertain weather conditions. Consequently, operators must accurately forecast wind speed/power in the dispatch center to carry out unit commitment, real power scheduling and economic dispatch. This work presents a novel method based on the integration of empirical mode decomposition (EMD with artificial neural networks (ANN to forecast the short-term (1 h ahead wind speed/power. First, significant parameters for training the ANN are identified using the correlation coefficients. These significant parameters serve as inputs of the ANN. Owing to the volatile and intermittent wind speed/power, the historical time series of wind speed/power is decomposed into several intrinsic mode functions (IMFs and a residual function through EMD. Each IMF becomes less volatile and therefore increases the accuracy of the neural network. The final forecasting results are achieved by aggregating all individual forecasting results from all IMFs and their corresponding residual functions. Real data related to the wind speed and wind power measured at a wind-turbine generator in Taiwan are used for simulation. The wind speed forecasting and wind power forecasting for the four seasons are studied. Comparative studies between the proposed method and traditional methods (i.e., artificial neural network without EMD, autoregressive integrated moving average (ARIMA, and persistence method are also introduced.

  17. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

  18. A hybrid approach for short-term forecasting of wind speed.

    Science.gov (United States)

    Tatinati, Sivanagaraja; Veluvolu, Kalyana C

    2013-01-01

    We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.

  19. On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation

    International Nuclear Information System (INIS)

    Wang, Yamin; Wu, Lei

    2016-01-01

    This paper presents a comprehensive analysis on practical challenges of empirical mode decomposition (EMD) based algorithms on wind speed and solar irradiation forecasts that have been largely neglected in literature, and proposes an alternative approach to mitigate such challenges. Specifically, the challenges are: (1) Decomposed sub-series are very sensitive to the original time series data. That is, sub-series of the new time series, consisting of the original one plus a limit number of new data samples, may significantly differ from those used in training forecasting models. In turn, forecasting models established by original sub-series may not be suitable for newly decomposed sub-series and have to be trained more frequently; and (2) Key environmental factors usually play a critical role in non-decomposition based methods for forecasting wind speed and solar irradiation. However, it is difficult to incorporate such critical environmental factors into forecasting models of individual decomposed sub-series, because the correlation between the original data and environmental factors is lost after decomposition. Numerical case studies on wind speed and solar irradiation forecasting show that the performance of existing EMD-based forecasting methods could be worse than the non-decomposition based forecasting model, and are not effective in practical cases. Finally, the approximated forecasting model based on EMD is proposed to mitigate the challenges and achieve better forecasting results than existing EMD-based forecasting algorithms and the non-decomposition based forecasting models on practical wind speed and solar irradiation forecasting cases. - Highlights: • Two challenges of existing EMD-based forecasting methods are discussed. • Significant changes of sub-series in each step of the rolling forecast procedure. • Difficulties in incorporating environmental factors into sub-series forecasting models. • The approximated forecasting method is proposed to

  20. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.

    Science.gov (United States)

    Ren, Ye; Suganthan, Ponnuthurai Nagaratnam; Srikanth, Narasimalu

    2016-08-01

    Wind energy is a clean and an abundant renewable energy source. Accurate wind speed forecasting is essential for power dispatch planning, unit commitment decision, maintenance scheduling, and regulation. However, wind is intermittent and wind speed is difficult to predict. This brief proposes a novel wind speed forecasting method by integrating empirical mode decomposition (EMD) and support vector regression (SVR) methods. The EMD is used to decompose the wind speed time series into several intrinsic mode functions (IMFs) and a residue. Subsequently, a vector combining one historical data from each IMF and the residue is generated to train the SVR. The proposed EMD-SVR model is evaluated with a wind speed data set. The proposed EMD-SVR model outperforms several recently reported methods with respect to accuracy or computational complexity.

  1. An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2015-01-01

    Full Text Available With depletion of traditional energy and increasing environmental problems, wind energy, as an alternative renewable energy, has drawn more and more attention internationally. Meanwhile, wind is plentiful, clean, and environmentally friendly; moreover, its speed is a very important piece of information needed in the operations and planning of the wind power system. Therefore, choosing an effective forecasting model with good performance plays a quite significant role in wind power system. A hybrid CS-EEMD-FNN model is firstly proposed in this paper for multistep ahead prediction of wind speed, in which EEMD is employed as a data-cleaning method that aims to remove the high frequency noise embedded in the wind speed series. CS optimization algorithm is used to select the best parameters in the FNN model. In order to evaluate the effectiveness and performance of the proposed hybrid model, three other short-term wind speed forecasting models, namely, FNN model, EEMD-FNN model, and CS-FNN model, are carried out to forecast wind speed using data measured at a typical site in Shandong wind farm, China, over three seasons in 2011. Experimental results demonstrate that the developed hybrid CS-EEMD-FNN model outperforms other models with more accuracy, which is suitable to wind speed forecasting in this area.

  2. A hybrid wavelet transform based short-term wind speed forecasting approach.

    Science.gov (United States)

    Wang, Jujie

    2014-01-01

    It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

  3. A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

    International Nuclear Information System (INIS)

    Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

    2014-01-01

    Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules

  4. Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin

    2014-02-27

    To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.

  5. Fine tuning support vector machines for short-term wind speed forecasting

    International Nuclear Information System (INIS)

    Zhou Junyi; Shi Jing; Li Gong

    2011-01-01

    Research highlights: → A systematic approach to tuning SVM models for wind speed prediction is proposed. → Multiple kernel functions and a wide range of tuning parameters are evaluated, and optimal parameters for each kernel function are obtained. → It is found that the forecasting performance of SVM is closely related to the dynamic characteristics of wind speed. → Under the optimal combination of parameters, different kernels give comparable forecasting accuracy. -- Abstract: Accurate forecasting of wind speed is critical to the effective harvesting of wind energy and the integration of wind power into the existing electric power grid. Least-squares support vector machines (LS-SVM), a powerful technique that is widely applied in a variety of classification and function estimation problems, carries great potential for the application of short-term wind speed forecasting. In this case, tuning the model parameters for optimal forecasting accuracy is a fundamental issue. This paper, for the first time, presents a systematic study on fine tuning of LS-SVM model parameters for one-step ahead wind speed forecasting. Three SVM kernels, namely linear, Gaussian, and polynomial kernels, are implemented. The SVM parameters considered include the training sample size, SVM order, regularization parameter, and kernel parameters. The results show that (1) the performance of LS-SVM is closely related to the dynamic characteristics of wind speed; (2) all parameters investigated greatly affect the performance of LS-SVM models; (3) under the optimal combination of parameters after fine tuning, the three kernels give comparable forecasting accuracy; (4) the performance of linear kernel is worse than the other two kernels when the training sample size or SVM order is small. In addition, LS-SVMs are compared against the persistence approach, and it is found that they can outperform the persistence model in the majority of cases.

  6. Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

    Science.gov (United States)

    AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

    2014-12-01

    Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using

  7. Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China

    International Nuclear Information System (INIS)

    Sun, Wei; Liu, Mohan

    2016-01-01

    Highlights: • FEEMD–RELM is proposed for wind speed forecasting. • Short-term and mid-term wind speed are forecasted by the proposed model. • PACF is introduced to select the input of RELM. • Three cases in Hebei province are applied in this paper. - Abstract: Reducing the dependence on fossil-fuel-based resources is becoming significant due to the detrimental effects on environment and global energy-dependent. Thus, increased attention has been paid to wind power, a type of clean and renewable energy. However, owing to the stochastic nature of wind speed, it is essential to build a wind speed forecasting model with high-precision for wind power utilization. Therefore, this paper proposes a hybrid model which combines fast ensemble empirical model decomposition (FEEMD) with regularized extreme learning machine (RELM). The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series. Then RELM is built to forecast the sub-series. Partial auto correlation function (PACF) is applied to analyze the intrinsic relationships between the historical speeds so as to select the inputs of RELM. To verify the developed models, short-term wind speed data in July 2010 and monthly data from January 2000 to May 2010 in Hong songwa wind farm, Chengde city are used for model construction and testing. Two additional forecasting cases in Hebei province are also applied to prove the model’s validity. The simulation test results show that the built model is effective, efficient and practicable.

  8. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    Science.gov (United States)

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  9. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

    Science.gov (United States)

    Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

  10. Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2013-04-01

    The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more

  11. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Zhang, Wenyu; Qu, Zongxi; Zhang, Kequan; Mao, Wenqian; Ma, Yining; Fan, Xu

    2017-01-01

    Highlights: • A CEEMDAN-CLSFPA combined model is proposed for short-term wind speed forecasting. • The CEEMDAN technique is used to decompose the original wind speed series. • A modified optimization algorithm-CLSFPA is proposed to optimize the weights of the combined model. • The no negative constraint theory is applied to the combined model. • Robustness of the proposed model is validated by data sampled from four different wind farms. - Abstract: Wind energy, which is stochastic and intermittent by nature, has a significant influence on power system operation, power grid security and market economics. Precise and reliable wind speed prediction is vital for wind farm planning and operational planning for power grids. To improve wind speed forecasting accuracy, a large number of forecasting approaches have been proposed; however, these models typically do not account for the importance of data preprocessing and are limited by the use of individual models. In this paper, a novel combined model – combining complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), flower pollination algorithm with chaotic local search (CLSFPA), five neural networks and no negative constraint theory (NNCT) – is proposed for short-term wind speed forecasting. First, a recent CEEMDAN is employed to divide the original wind speed data into a finite set of IMF components, and then a combined model, based on NNCT, is proposed for forecasting each decomposition signal. To improve the forecasting capacity of the combined model, a modified flower pollination algorithm (FPA) with chaotic local search (CLS) is proposed and employed to determine the optimal weight coefficients of the combined model, and the final prediction values were obtained by reconstructing the refined series. To evaluate the forecasting ability of the proposed combined model, 15-min wind speed data from four wind farms in the eastern coastal areas of China are used. The experimental results of

  12. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

    2014-01-01

    Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

  13. A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

    Directory of Open Access Journals (Sweden)

    Marino Marrocu

    2017-11-01

    Full Text Available An accurate forecast of the power generated by a wind turbine is of paramount importance for its optimal exploitation. Several forecasting methods have been proposed either based on a physical modeling or using a statistical approach. All of them rely on the availability of high quality measures of local wind speed, corresponding generated power and on numerical weather forecasts. In this paper, a simple and effective wind power forecast technique, based on the probability distribution mapping of wind speed forecast and observed power data, is presented and it is applied to two turbines located on the island of Borkum (Germany in the North Sea. The wind speed forecast of the ECMWF model at 100 m from the ground is used as the prognostic meteorological parameter. Training procedures are based entirely on relatively short time series of power measurements. Results show that our approach has skills that are similar or better than those obtained using more standard methods when measured with mean absolute error.

  14. Short-term Probabilistic Forecasting of Wind Speed Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Iversen, Jan Emil Banning; Morales González, Juan Miguel; Møller, Jan Kloppenborg

    2016-01-01

    It is widely accepted today that probabilistic forecasts of wind power production constitute valuable information for both wind power producers and power system operators to economically exploit this form of renewable energy, while mitigating the potential adverse effects related to its variable...... and uncertain nature. In this paper, we propose a modeling framework for wind speed that is based on stochastic differential equations. We show that stochastic differential equations allow us to naturally capture the time dependence structure of wind speed prediction errors (from 1 up to 24 hours ahead) and......, most importantly, to derive point and quantile forecasts, predictive distributions, and time-path trajectories (also referred to as scenarios or ensemble forecasts), all by one single stochastic differential equation model characterized by a few parameters....

  15. Verification of high-speed solar wind stream forecasts using operational solar wind models

    DEFF Research Database (Denmark)

    Reiss, Martin A.; Temmer, Manuela; Veronig, Astrid M.

    2016-01-01

    and the background solar wind conditions. We found that both solar wind models are capable of predicting the large-scale features of the observed solar wind speed (root-mean-square error, RMSE ≈100 km/s) but tend to either overestimate (ESWF) or underestimate (WSA) the number of high-speed solar wind streams (threat...

  16. A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

    Directory of Open Access Journals (Sweden)

    Stylianos Sp. Pappas

    2014-01-01

    Full Text Available An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF, each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

  17. Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

    Science.gov (United States)

    Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

    2009-01-01

    The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.

  18. Linear and non-linear autoregressive models for short-term wind speed forecasting

    International Nuclear Information System (INIS)

    Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

    2016-01-01

    Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

  19. The use of Markov chains in forecasting wind speed: Matlab source code and applied case study

    Directory of Open Access Journals (Sweden)

    Ionuţ Alexandru Petre

    2017-01-01

    Full Text Available The ability to predict the wind speed has an important role for renewable energy industry which relies on wind speed forecasts in order to calculate the power a wind farm can produce in an area. There are several well-known methods to predict wind speed, but in this paper we focus on short-term wind forecasting using Markov chains. Often gaps can be found in the time series of the wind speed measurements and repeating the measurements is usually not a valid option. In this study it is shown that using Markov chains these gaps from the time series can be filled (they can be generated in an efficient way, but only when the missing data is for a short period of time. Also, the developed Matlab programms that are used in the case study, are included in the paper beeing presented and commented by the authors. In the case study data from a wind farm in Italy is used. The available data are as average wind speed at an interval of 10 minutes in the time period 11/23/2005 - 4/27/2006.

  20. Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-06-01

    Full Text Available Affected by various environmental factors, wind speed presents high fluctuation, nonlinear and non-stationary characteristics. To evaluate wind energy properly and efficiently, this paper proposes a modified fast ensemble empirical model decomposition (FEEMD-bat algorithm (BA-least support vector machines (LSSVM (FEEMD-BA-LSSVM model combined with input selected by deep quantitative analysis. The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs with one residual series. Then a LSSVM is built to forecast these sub-series. In order to select input from environment variables, Cointegration and Granger causality tests are proposed to check the influence of temperature with different leading lengths. Partial correlation is applied to analyze the inner relationships between the historical speeds thus to select the LSSVM input. The parameters in LSSVM are fine-tuned by BA to ensure the generalization of LSSVM. The forecasting results suggest the hybrid approach outperforms the compared models.

  1. A comparison of regression algorithms for wind speed forecasting at Alexander Bay

    CSIR Research Space (South Africa)

    Botha, Nicolene

    2016-12-01

    Full Text Available to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting...

  2. Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

    DEFF Research Database (Denmark)

    Pinson, Pierre; Hagedorn, Renate

    2012-01-01

    A framework for the verification of ensemble forecasts of near-surface wind speed is described. It is based on existing scores and diagnostic tools, though considering observations from synoptic stations as reference instead of the analysis. This approach is motivated by the idea of having a user...

  3. Analysis of Wind Speed Forecasting Error Effects on Automatic Generation Control Performance

    Directory of Open Access Journals (Sweden)

    H. Rajabi Mashhadi

    2014-09-01

    Full Text Available The main goal of this paper is to study statistical indices and evaluate AGC indices in power system which has large penetration of the WTGs. Increasing penetration of wind turbine generations, needs to study more about impacts of it on power system frequency control. Frequency control is changed with unbalancing real-time system generation and load . Also wind turbine generations have more fluctuations and make system more unbalance. Then AGC loop helps to adjust system frequency and the scheduled tie-line powers. The quality of AGC loop is measured by some indices. A good index is a proper measure shows the AGC performance just as the power system operates. One of well-known measures in literature which was introduced by NERC is Control Performance Standards(CPS. Previously it is claimed that a key factor in CPS index is related to standard deviation of generation error, installed power and frequency response. This paper focuses on impact of a several hours-ahead wind speed forecast error on this factor. Furthermore evaluation of conventional control performances in the power systems with large-scale wind turbine penetration is studied. Effects of wind speed standard deviation and also degree of wind farm penetration are analyzed and importance of mentioned factor are criticized. In addition, influence of mean wind speed forecast error on this factor is investigated. The study system is a two area system which there is significant wind farm in one of those. The results show that mean wind speed forecast error has considerable effect on AGC performance while the mentioned key factor is insensitive to this mean error.

  4. Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting

    DEFF Research Database (Denmark)

    Gallego, Cristobal; Pinson, Pierre; Madsen, Henrik

    2011-01-01

    on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power......Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some...... of these effects by means of statistical models. To this end, a benchmarking between two different families of varyingcoefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused...

  5. Recursive wind speed forecasting based on Hammerstein Auto-Regressive model

    International Nuclear Information System (INIS)

    Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier

    2015-01-01

    Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling

  6. Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2018-02-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  7. Comparison of statistical post-processing methods for probabilistic wind speed forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2017-12-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  8. Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

    International Nuclear Information System (INIS)

    Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

    2016-01-01

    Highlights: • We propose a spatio-temporal approach for wind speed forecasting. • The method is based on a combination of Wavelet decomposition and structured-sparse recovery. • Our analyses confirm that low-dimensional structures govern the interactions between stations. • Our method particularly shows improvements for profiles with high ramps. • We examine our approach on real data and illustrate its superiority over a set of benchmark models. - Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.

  9. A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Zongxi Qu

    2016-01-01

    Full Text Available As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

  10. Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2015-01-01

    Full Text Available Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC analysis and a support vector regression (SVR model that is coupled with brainstorm optimization (BSO and cuckoo search (CS algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.

  11. An advanced strategy for wind speed forecasting using expert 2-D FIR filters

    Directory of Open Access Journals (Sweden)

    MOGHADDAM, A. A.

    2010-11-01

    Full Text Available Renewable energies such as wind and solar have become the most attractive means of electricity generation nowadays. Social and environmental benefits as well as economical issues result in further utilization of such these energy resources. In this regard, wind energy plays an important roll in operation of small-scale power systems like Micro Grid. On the other hand, wind stochastic nature in different time and place horizons, makes accurate forecasting of its behavior an inevitable task for market planners and energy management systems. In this paper an advanced strategy for wind speed estimation has been purposed and its superior performance is compared to that of conventional methods. The model is based on linear predictive filtering and image processing principles using 2-D FIR filters. To show the efficiency of purposed predictive model different FIR filters are designed and tested through similar data. Wind speed data have been collected during the period January 1, 2009 to December 31, 2009 from Casella automatic weather station at Plymouth. It is observed that 2-D FIR filters act more accurately in comparison with 1-D conventional representations; however, their prediction ability varies considerably through different filter sizing.

  12. Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Jiani Heng

    2016-03-01

    Full Text Available Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid [k, Bt] -ABBP (back propagation based on adaptive strategy with parameters k and Bt model was developed based on an adaptive boosting (AB strategy that integrates several BP (back propagation neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm incorporating a conjugate gradient (CG is proposed for searching for the optimal parameters of the [k, Bt] -ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

  13. A new method for wind speed forecasting based on copula theory.

    Science.gov (United States)

    Wang, Yuankun; Ma, Huiqun; Wang, Dong; Wang, Guizuo; Wu, Jichun; Bian, Jinyu; Liu, Jiufu

    2018-01-01

    How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Statistical Short-Range Guidance for Peak Wind Speed Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase I Results

    Science.gov (United States)

    Lambert, Winifred C.; Merceret, Francis J. (Technical Monitor)

    2002-01-01

    This report describes the results of the ANU's (Applied Meteorology Unit) Short-Range Statistical Forecasting task for peak winds. The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The Keith Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A 7 year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. In all climatologies, the average and peak wind speeds were highly variable in time. This indicated that the development of a peak wind forecasting tool would be difficult. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. The climatologies and PDFs provide tools with which to make peak wind forecasts that are critical to safe operations.

  15. A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Yuyang Gao

    2016-09-01

    Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

  16. Forecasting and simulating wind speed in Corsica by using an autoregressive model

    International Nuclear Information System (INIS)

    Poggi, P.; Muselli, M.; Notton, G.; Cristofari, C.; Louche, A.

    2003-01-01

    Alternative approaches for generating wind speed time series are discussed. The method utilized involves the use of an autoregressive process model. The model has been applied to three Mediterranean sites in Corsica and has been used to generate 3-hourly synthetic time series for these considered sites. The synthetic time series have been examined to determine their ability to preserve the statistical properties of the Corsican wind speed time series. In this context, using the main statistical characteristics of the wind speed (mean, variance, probability distribution, autocorrelation function), the data simulated are compared to experimental ones in order to check whether the wind speed behavior was correctly reproduced over the studied periods. The purpose is to create a data generator in order to construct a reference year for wind systems simulation in Corsica

  17. A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

    Directory of Open Access Journals (Sweden)

    Feiyu Zhang

    2016-06-01

    Full Text Available Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method.

  18. On forecasting ionospheric total electron content responses to high-speed solar wind streams

    Directory of Open Access Journals (Sweden)

    Meng Xing

    2016-01-01

    Full Text Available Conditions in the ionosphere have become increasingly important to forecast, since more and more spaceborne and ground-based technological systems rely on ionospheric weather. Here we explore the feasibility of ionospheric forecasts with the current generation of physics-based models. In particular, we focus on total electron content (TEC predictions using the Global Ionosphere-Thermosphere Model (GITM. Simulations are configured in a forecast mode and performed for four typical high-speed-stream events during 2007–2012. The simulated TECs are quantified through a metric, which divides the globe into a number of local regions and robustly differentiates between quiet and disturbed periods. Proposed forecast products are hourly global maps color-coded by the TEC disturbance level of each local region. To assess the forecasts, we compare the simulated TEC disturbances with global TEC maps derived from Global Positioning System (GPS satellite observations. The forecast performance is found to be merely acceptable, with a large number of regions where the observed variations are not captured by the simulations. Examples of model-data agreements and disagreements are investigated in detail, aiming to understand the model behavior and improve future forecasts. For one event, we identify two adjacent regions with similar TEC observations but significant differences in how local chemistry versus plasma transport contribute to electron density changes in the simulation. Suggestions for further analysis are described.

  19. Forecasting Downdraft Wind Speeds Associated with Airmass Thunderstorms for Peterson Air Force Base, Colorado, Using the WSR-88D

    National Research Council Canada - National Science Library

    Steen, Travis

    1999-01-01

    .... During the same summer, Air Force Space Command units issued nearly 65% of their weather warnings for convective winds, making the forecasting of convective winds the most frequent challenge to forecasters...

  20. Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs

    International Nuclear Information System (INIS)

    Hong, Ying-Yi; Chang, Huei-Lin; Chiu, Ching-Sheng

    2010-01-01

    Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown.

  1. Noise model based ν-support vector regression with its application to short-term wind speed forecasting.

    Science.gov (United States)

    Hu, Qinghua; Zhang, Shiguang; Xie, Zongxia; Mi, Jusheng; Wan, Jie

    2014-09-01

    Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search

    Directory of Open Access Journals (Sweden)

    Xuejun Chen

    2015-01-01

    Full Text Available The support vector regression (SVR and neural network (NN are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H and wavelet denoising (WD techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.

  3. A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Zhang, Chu; Zhou, Jianzhong; Li, Chaoshun; Fu, Wenlong; Peng, Tian

    2017-01-01

    Highlights: • A novel hybrid approach is proposed for wind speed forecasting. • The variational mode decomposition (VMD) is optimized to decompose the original wind speed series. • The input matrix and parameters of ELM are optimized simultaneously by using a hybrid BSA. • Results show that OVMD-HBSA-ELM achieves better performance in terms of prediction accuracy. - Abstract: Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the

  4. Utility operating strategy and requirements for wind power forecast

    Science.gov (United States)

    Dub, W.; Pape, H.

    1983-06-01

    The commitment of a generation system including wind energy conversion systems will be based on wind speed and wind power forecasts. Forecasts for time spans of equal length with the startup/shutdown times of conventional units will be of great importance. The paper discusses forecast horizons up to 3 hours and 6 hours respectively. In addition, the problem of getting good wind speed forecasts is investigated by fitting time series models to wind speed data. Finally, the impact of hypothetical perfect forecasts on the commitment of intermediate load units is demonstrated by means of the wind power variations within spans up to 3 hours.

  5. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2016-01-01

    Full Text Available Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD, which reduces the effect of noise on the wind speed data; (II artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM model are optimized by the cuckoo search (CS algorithm; (III parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

  6. An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2017-07-01

    Full Text Available Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics.

  7. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

    Science.gov (United States)

    Yang, Ben; Qian, Yun; Berg, Larry K.; Ma, Po-Lun; Wharton, Sonia; Bulaevskaya, Vera; Yan, Huiping; Hou, Zhangshuan; Shaw, William J.

    2017-01-01

    We evaluate the sensitivity of simulated turbine-height wind speeds to 26 parameters within the Mellor-Yamada-Nakanishi-Niino (MYNN) planetary boundary-layer scheme and MM5 surface-layer scheme of the Weather Research and Forecasting model over an area of complex terrain. An efficient sampling algorithm and generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of simulated turbine-height wind speeds. The results indicate that most of the variability in the ensemble simulations is due to parameters related to the dissipation of turbulent kinetic energy (TKE), Prandtl number, turbulent length scales, surface roughness, and the von Kármán constant. The parameter associated with the TKE dissipation rate is found to be most important, and a larger dissipation rate produces larger hub-height wind speeds. A larger Prandtl number results in smaller nighttime wind speeds. Increasing surface roughness reduces the frequencies of both extremely weak and strong airflows, implying a reduction in the variability of wind speed. All of the above parameters significantly affect the vertical profiles of wind speed and the magnitude of wind shear. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability.

  8. A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed

    Directory of Open Access Journals (Sweden)

    Pei Du

    2016-08-01

    Full Text Available With the limitations of conventional energy becoming increasing distinct, wind energy is emerging as a promising renewable energy source that plays a critical role in the modern electric and economic fields. However, how to select optimization algorithms to forecast wind speed series and improve prediction performance is still a highly challenging problem. Traditional single algorithms are widely utilized to select and optimize parameters of neural network algorithms, but these algorithms usually ignore the significance of parameter optimization, precise searching, and the application of accurate data, which results in poor forecasting performance. With the aim of overcoming the weaknesses of individual algorithms, a novel hybrid algorithm was created, which can not only easily obtain the real and effective wind speed series by using singular spectrum analysis, but also possesses stronger adaptive search and optimization capabilities than the other algorithms: it is faster, has fewer parameters, and is less expensive. For the purpose of estimating the forecasting ability of the proposed combined model, 10-min wind speed series from three wind farms in Shandong Province, eastern China, are employed as a case study. The experimental results were considerably more accurately predicted by the presented algorithm than the comparison algorithms.

  9. Rejoinder on: Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin

    2014-02-27

    We are thankful to the four reviewers for providing very valuable and insightful comments. We have divided our rejoinder into two main parts: (1) the rotating RSTD model; and (2) the integration of wind power into a power system. In each part, we present our views on the various comments of the discussants and provide further discussion. © 2014 Sociedad de Estadística e Investigación Operativa.

  10. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ben; Qian, Yun; Berg, Larry K.; Ma, Po-Lun; Wharton, Sonia; Bulaevskaya, Vera; Yan, Huiping; Hou, Zhangshuan; Shaw, William J.

    2016-07-21

    We evaluate the sensitivity of simulated turbine-height winds to 26 parameters applied in a planetary boundary layer (PBL) scheme and a surface layer scheme of the Weather Research and Forecasting (WRF) model over an area of complex terrain during the Columbia Basin Wind Energy Study. An efficient sampling algorithm and a generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of modeled turbine-height winds. The results indicate that most of the variability in the ensemble simulations is contributed by parameters related to the dissipation of the turbulence kinetic energy (TKE), Prandtl number, turbulence length scales, surface roughness, and the von Kármán constant. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability. The parameter associated with the TKE dissipation rate is found to be the most important one, and a larger dissipation rate can produce larger hub-height winds. A larger Prandtl number results in weaker nighttime winds. Increasing surface roughness reduces the frequencies of both extremely weak and strong winds, implying a reduction in the variability of the wind speed. All of the above parameters can significantly affect the vertical profiles of wind speed, the altitude of the low-level jet and the magnitude of the wind shear strength. The wind direction is found to be modulated by the same subset of influential parameters. Remainder of abstract is in attachment.

  11. Wind forecasting for grid code compliance

    Energy Technology Data Exchange (ETDEWEB)

    Vanitha, V.; Kishore, S.R.N. [Amrita Vishwa Vidyapeetham Univ.. Dept. of Electrical and Electronics Engineering, Coimbatore (India)

    2012-07-01

    This work explores Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the average hourly wind speed. To determine the characteristics of ANFIS that best suited the target wind speed forecasting system, several ANFIS models were trained, tested and compared. Different types and number of inputs, training and checking sizes, type and number of membership functions and techniques to generate the initial (FIS) were analyzed. Comparisons with other forecasting methods were analyzed the models were given wind speed, direction and air pressure as inputs having the best forecasting accuracy. SCADA system is utilized to obtain the wind speed to the forecasting system in the host computer where ANFIS is present. The SCADA is located in the central room, the substation of the wind farm, or even at a remote off site point. The data obtained from the site is plotted at every instant and the predicted wind speed is displayed and also exported to the excel sheet which will be sent/e-mailed in the form of Graphs and excel sheets to the operator, State load dispatch centre (SLDC) and to the customer. (Author)

  12. Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

    Directory of Open Access Journals (Sweden)

    Chih-Chiang Wei

    2018-04-01

    Full Text Available Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR, the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1 to develop data-driven techniques and a powerful artificial neural network (ANN model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2 to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau, and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1 The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2 when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3 regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as

  13. Integration of wind generation forecasts. Volume 2

    International Nuclear Information System (INIS)

    Ahlstrom, M.; Zavadil, B.; Jones, L.

    2005-01-01

    WindLogics is a company that specializes in atmospheric modelling, visualization and fine-scale forecasting systems for the wind power industry. A background of the organization was presented. The complexities of wind modelling were discussed. Issues concerning location and terrain, shear, diurnal and interannual variability were reviewed. It was suggested that wind power producers should aim to be mainstream, and that variability should be considered as intrinsic to fuel supply. Various utility operating impacts were outlined. Details of an Xcel NSP wind integration study were presented, as well as a studies conducted in New York state and Colorado. It was concluded that regulations and load following impacts with wind energy integration are modest. Overall impacts are dominated by costs incurred to accommodate wind generation variability and uncertainty in the day-ahead time frame. Cost impacts can be reduced with adjustments to operating strategies, improvements in wind forecasting and access to real-time markets. Details of WindLogic's wind energy forecast system were presented, as well as examples of day ahead and hour ahead forecasts and wind speed and power forecasts. Screenshots of control room integration, EMS integration and simulations were presented. Details of a utility-scale wind energy forecasting system funded by Xcel Renewable Development Fund (RDF) were also presented. The goal of the system was to optimize the way that wind forecast information is integrated into the control room environment. Project components were outlined. It was concluded that accurate day-ahead forecasting can lead to significant asset optimization. It was recommended that wind plants share data, and aim to resolve issues concerning grid codes and instrumentation. refs., tabs., figs

  14. Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg

    forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...

  15. Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

    Science.gov (United States)

    Williams, John L.; Maxwell, Reed M.; Monache, Luca Delle

    2013-12-01

    Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

  16. A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Hu, Jianming

    2015-01-01

    With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. - Highlights: • The proposed approach can make probabilistic modeling for wind speed series. • The proposed approach adapts to the time-varying characteristic of the wind speed. • The hybrid approach can extract the meaningful components from the wind speed series. • The proposed method can generate adaptive, reliable and more accurate forecasting results. • The proposed model combines four independent forecasting engines in a nonlinear way.

  17. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  18. Improving weather forecasts for wind energy applications

    International Nuclear Information System (INIS)

    Kay, Merlinde; MacGill, Iain

    2010-01-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  19. Deterministic prediction of surface wind speed variations

    Directory of Open Access Journals (Sweden)

    G. V. Drisya

    2014-11-01

    Full Text Available Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

  20. Wind_Speeds_Master

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set included wind speeds for each subregion in the study (Georges Bank, Gulf of Maine, Southern New England, Middle Atlantic Bight) . The data came from...

  1. Short time ahead wind power production forecast

    International Nuclear Information System (INIS)

    Sapronova, Alla; Meissner, Catherine; Mana, Matteo

    2016-01-01

    An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast. (paper)

  2. Peak Wind Tool for General Forecasting

    Science.gov (United States)

    Barrett, Joe H., III

    2010-01-01

    The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded

  3. Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

    DEFF Research Database (Denmark)

    Draxl, Caroline; Hahmann, Andrea N.; Pena Diaz, Alfredo

    2014-01-01

    with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights...... regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by theWeather Research and Forecasting model using seven sets of simulations...

  4. High-speed Solar Wind Stream Forecast Based on Coronal Hole Index Derived from Solar EUV Images

    Science.gov (United States)

    Gong, J.; Luo, B.; Bu, X.; Liu, S.

    2017-12-01

    High-speed streams (HSS), which originate from coronal holes on the Sun, are interplanetary sources of recurrent geospace environment disturbances such as geomagnetic storms, relativistic electron enhancements at the geosynchronous orbit, and thermosphere density enhancements which increase the orbit decay rate for low orbit satellites. People have been searching for good indices which can be used as proxies of coronal hole to predict HSS. Among these indices, the Pch reported by Luo et al. [2008], reflected both the area and the brightness contributions of coronal hole and showed potential in predicting HSS. In this study, we evaluate the performance of the Pch index in predict the solar wind speed at L1, using Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images for years 2011 to 2016. On verification of the predicting capability, we focus on the event-based analysis of the predicted arrival times and amplitudes of high-speed streams (considered as HSS events). It is found that the Pch index is capable of predicting the large-scale high-speed stream features about 4 days in advance, with uncertainties in the HSS arrival time of about 1 day and uncertainties in the speed of about 100 km/s.

  5. IEA Wind Task 36 Forecasting

    Science.gov (United States)

    Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

    2017-04-01

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts

  6. Application and verification of ECMWF seasonal forecast for wind energy

    Science.gov (United States)

    Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

    2015-04-01

    A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power

  7. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting in the Washington-Oregon Region

    Energy Technology Data Exchange (ETDEWEB)

    Zack, John [AWS Truewind, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truewind, LLC, Albany, NY (United States); Young, Steve [AWS Truewind, LLC, Albany, NY (United States); Knowe, Glenn Van [AWS Truewind, LLC, Albany, NY (United States); Waight, Ken [AWS Truewind, LLC, Albany, NY (United States); Manobianco, John [AWS Truewind, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2010-10-01

    To economically and reliably balance electrical load and generation, electrical grid operators, also called Balancing Authorities (BA), need highly accurate electrical power generation forecasts in time frames ranging from a few minutes to six hours ahead. As wind power generation increases, there is a requirement to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts covering this short look-ahead period have depended heavily on short-term trends obtained from the actual power production and meteorological data of a wind generation facility. Additional data are often available from Numerical Weather Prediction (NWP) models and sometimes from off-site meteorological towers near wind generation facilities.

  8. Wind power forecast error smoothing within a wind farm

    International Nuclear Information System (INIS)

    Saleck, Nadja; Bremen, Lueder von

    2007-01-01

    Smoothing of wind power forecast errors is well-known for large areas. Comparable effects within a wind farm are investigated in this paper. A Neural Network was taken to predict the power output of a wind farm in north-western Germany comprising 17 turbines. A comparison was done between an algorithm that fits mean wind and mean power data of the wind farm and a second algorithm that fits wind and power data individually for each turbine. The evaluation of root mean square errors (RMSE) shows that relative small smoothing effects occur. However, it can be shown for this wind farm that individual calculations have the advantage that only a few turbines are needed to give better results than the use of mean data. Furthermore different results occurred if predicted wind speeds are directly fitted to observed wind power or if predicted wind speeds are first fitted to observed wind speeds and then applied to a power curve. The first approach gives slightly better RMSE values, the bias improves considerably

  9. The Application of TAPM for Site Specific Wind Energy Forecasting

    Directory of Open Access Journals (Sweden)

    Merlinde Kay

    2016-02-01

    Full Text Available The energy industry uses weather forecasts for determining future electricity demand variations due to the impact of weather, e.g., temperature and precipitation. However, as a greater component of electricity generation comes from intermittent renewable sources such as wind and solar, weather forecasting techniques need to now also focus on predicting renewable energy supply, which means adapting our prediction models to these site specific resources. This work assesses the performance of The Air Pollution Model (TAPM, and demonstrates that significant improvements can be made to only wind speed forecasts from a mesoscale Numerical Weather Prediction (NWP model. For this study, a wind farm site situated in North-west Tasmania, Australia was investigated. I present an analysis of the accuracy of hourly NWP and bias corrected wind speed forecasts over 12 months spanning 2005. This extensive time frame allows an in-depth analysis of various wind speed regimes of importance for wind-farm operation, as well as extreme weather risk scenarios. A further correction is made to the basic bias correction to improve the forecast accuracy further, that makes use of real-time wind-turbine data and a smoothing function to correct for timing-related issues. With full correction applied, a reduction in the error in the magnitude of the wind speed by as much as 50% for “hour ahead” forecasts specific to the wind-farm site has been obtained.

  10. Forecast of Wind Speed with a Backpropagation Artificial Neural Network in the Isthmus of Tehuantepec Region in the State of Oaxaca, Mexico

    Directory of Open Access Journals (Sweden)

    Orlando Lastres Danguillecourt

    2012-03-01

    Full Text Available Este trabajo presenta los resultados preliminares de la configuración de una red neuronal artificial (ANN, de tipo alimentación hacia adelante con el método de entrenamiento de retro-propagación para pronosticar la velocidad de viento en la región del Istmo de Tehuantepec, Oaxaca, México. La base de datos utilizada abarca los años comprendidos entre Junio 2008- Noviembre 2011, y fue suministrada por una estación meteorológica ubicada en la Universidad del Istmo campus Tehuantepec. Los experimentos se realizaron utilizando las siguientes variables: velocidad del viento, presión, temperatura y fecha. Al mismo tiempo se hicieron siete pruebas combinando estas variables, comparando su error cuadrático medio (MSE y el coeficiente de correlación r, con los datos de predicción y experimentales. En esta investigación, se propone una ANN de dos capas ocultas, para un pronóstico de 48 horas.This paper presents the preliminary results of setting up an artificial neural network (ANN of the feed forward type with the backpropagation training method for forecast wind speed in the region in the Isthmus of Tehuantepec, Oaxaca, Mexico. The database used covers the years from June 2008 - November 2011, and was supplied by a meteorological station located at the Isthmus University campus Tehuantepec. The experiments were done using the following variables: wind speed, pressure, temperature and date. At the same time were done seven tests combining these variables, comparing their mean square error (MSE and coefficient correlation r, with data the predicting and experimental. In this research, is proposed a ANN of two hidden layers, for a forecast of 48 hours.

  11. Wind Speed Perception and Risk

    Science.gov (United States)

    Agdas, Duzgun; Webster, Gregory D.; Masters, Forrest J.

    2012-01-01

    Background How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human–wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks. Method We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s) winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt. Results Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk). The number of tropical cyclones people had experienced moderated the strength of the actual–perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms. Conclusion These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters. PMID:23226230

  12. Wind speed perception and risk.

    Directory of Open Access Journals (Sweden)

    Duzgun Agdas

    Full Text Available BACKGROUND: How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human-wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks. METHOD: We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt. RESULTS: Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk. The number of tropical cyclones people had experienced moderated the strength of the actual-perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms. CONCLUSION: These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters.

  13. ECMWF and SSMI Global Surface Wind Speeds

    Science.gov (United States)

    Halpern, David; Hollingsworth, Anthony; Wentz, Frank

    1993-01-01

    Monthly mean, 2.5 deg - x 2.5 deg-resolution, 10-m height wind speeds from the Special Sensor Microwave Imager (SSMI) instrument and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast-analysis system are compared between 60 deg S and 60 deg N during 1988-1991. The SSMI data were uniformly processed while numerous changes were made to the ECMWF forecast-analysis system. The SSMI measurements, which were compared with moored-buoy wind observations, were considered to be a reference data set to evaluate the influence of the changes made to the ECMWF system upon the ECMWF surface wind speed over the ocean. A demonstrable yearly decrease of the difference between SSMI and ECMWF wind speeds occurred in the 10 deg S - 10 deg N region, including the 5 deg S - 5 deg N zone of the Pacific Ocean, where nearly all of the variations occurred in the 160 deg E - 160 deg W region. The apparent improvement of the ECMWF wind speed occurred at the same time as the yearly decrease of the equatorial Pacific SSMI wind speed, which was associated with the natural transition from La Nina to El Nino conditions. In the 10 deg S - 10 deg N tropical Atlantic, the ECMWF wind speed had a 4-year trend, which was not expected nor was it duplicated with the SSMI data. No yearly trend was found in the difference between SSMI and ECMWF surface wind speeds in middle latitudes of the northern and southern hemispheres. The magnitude of the differences between SSMI and ECMWF was 0.4 m s^(-1) or 100 percent larger in the northern than in the southern hemisphere extratropics. In two areas (Arabian Sea and North Atlantic Ocean) where ECMWF and SSMI wind speeds were compared to ship measurements, the ship data had much better agreement with the ECMWF analyses compared to SSMI data. In the 10 deg S - 10 deg N area the difference between monthly standard deviations of the daily wind speeds dropped significantly from 1988 to 1989, but remained constant at about 30 percent for the remaining

  14. Wind Resource Assessment and Forecast Planning with Neural Networks

    Directory of Open Access Journals (Sweden)

    Nicolus K. Rotich

    2014-06-01

    Full Text Available In this paper we built three types of artificial neural networks, namely: Feed forward networks, Elman networks and Cascade forward networks, for forecasting wind speeds and directions. A similar network topology was used for all the forecast horizons, regardless of the model type. All the models were then trained with real data of collected wind speeds and directions over a period of two years in the municipal of Puumala, Finland. Up to 70th percentile of the data was used for training, validation and testing, while 71–85th percentile was presented to the trained models for validation. The model outputs were then compared to the last 15% of the original data, by measuring the statistical errors between them. The feed forward networks returned the lowest errors for wind speeds. Cascade forward networks gave the lowest errors for wind directions; Elman networks returned the lowest errors when used for short term forecasting.

  15. Inclusion of routine wind and turbulence forecasts in the Savannah River Plant's emergency response capabilities

    International Nuclear Information System (INIS)

    Pendergast, M.M.; Gilhousen, D.B.

    1980-01-01

    The Savannah River Plant's emergency response computer system was improved by the implementation of automatic forecasts of wind and turbulence for periods up to 30 hours. The forecasts include wind direction, wind speed, and horizontal and vertical turbulence intensity at 10, 91, and 243 m above ground for the SRP area, and were obtained by using the Model Output Statistics (MOS) technique. A technique was developed and tested to use the 30-hour MOS forecasts of wind and turbulence issued twice daily from the National Weather Service at Suitland, Maryland, into SRP's emergency response program. The technique for combining MOS forecasts, persistence, and adjusted-MOS forecast is used to generate good forecasts any time of day. Wind speed and turbulence forecasts have been shown to produce smaller root mean square errors (RMSE) than forecasts of persistence for time periods over about two hours. For wind direction, the adjusted-MOS forecasts produce smaller RMSE than persistence for times greater than four hours

  16. Empirical investigation on using wind speed volatility to estimate the operation probability and power output of wind turbines

    International Nuclear Information System (INIS)

    Liu, Heping; Shi, Jing; Qu, Xiuli

    2013-01-01

    Highlights: ► Ten-minute wind speed and power generation data of an offshore wind turbine are used. ► An ARMA–GARCH-M model is built to simultaneously forecast wind speed mean and volatility. ► The operation probability and expected power output of the wind turbine are predicted. ► The integrated approach produces more accurate wind power forecasting than other conventional methods. - Abstract: In this paper, we introduce a quantitative methodology that performs the interval estimation of wind speed, calculates the operation probability of wind turbine, and forecasts the wind power output. The technological advantage of this methodology stems from the empowered capability of mean and volatility forecasting of wind speed. Based on the real wind speed and corresponding wind power output data from an offshore wind turbine, this methodology is applied to build an ARMA–GARCH-M model for wind speed forecasting, and then to compute the operation probability and the expected power output of the wind turbine. The results show that the developed methodology is effective, the obtained interval estimation of wind speed is reliable, and the forecasted operation probability and expected wind power output of the wind turbine are accurate

  17. NTF – wind speed comparison

    DEFF Research Database (Denmark)

    Vesth, Allan; Gómez Arranz, Paula

    The report describes measurements carried out on a given turbine. A comparison between wind speed on the met mast and Nacelle Wind speed are made and the results are presented on graphs and in a table. The data used for the comparison are the data that are same as used for the power curve report...

  18. Data mining for wind power forecasting

    OpenAIRE

    Fugon, Lionel; Juban, Jérémie; Kariniotakis, Georges

    2008-01-01

    International audience; Short-term forecasting of wind energy production up to 2-3 days ahead is recognized as a major contribution for reliable large-scale wind power integration. Increasing the value of wind generation through the improvement of prediction systems performance is recognised as one of the priorities in wind energy research needs for the coming years. This paper aims to evaluate Data Mining type of models for wind power forecasting. Models that are examined include neural netw...

  19. The new IEA Wind Task 36 on Wind Power Forecasting

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, Joel; Frank, Helmut

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind...... Energy tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, …), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement...... forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions....

  20. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

  1. Forecasting winds over nuclear power plants statistics

    International Nuclear Information System (INIS)

    Marais, Ch.

    1997-01-01

    In the event of an accident at nuclear power plant, it is essential to forecast the wind velocity at the level where the efflux occurs (about 100 m). At present meteorologists refine the wind forecast from the coarse grid of numerical weather prediction (NWP) models. The purpose of this study is to improve the forecasts by developing a statistical adaptation method which corrects the NWP forecasts by using statistical comparisons between wind forecasts and observations. The Multiple Linear Regression method is used here to forecast the 100 m wind at 12 and 24 hours range for three Electricite de France (EDF) sites. It turns out that this approach gives better forecasts than the NWP model alone and is worthy of operational use. (author)

  2. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting in the Tehachapi Region Winter Season

    Energy Technology Data Exchange (ETDEWEB)

    Zack, John [AWS Truepower, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truepower, LLC, Albany, NY (United States); Young, Steve [AWS Truepower, LLC, Albany, NY (United States); Van Knowe, Glenn [AWS Truepower, LLC, Albany, NY (United States); Waight, Ken [AWS Truepower, LLC, Albany, NY (United States); Manobainco, John [AWS Truepower, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2010-10-20

    This study extends the wind power forecast sensitivity work done by Zack et al. (2010a, b) in two prior research efforts. Zack et al. (2010a, b) investigated the relative predictive value and optimal combination of different variables/locations from correlated sensitivity patterns. Their work involved developing the Multiple Observation Optimization Algorithm (MOOA) and applying the algorithm to the results obtained from the Ensemble Sensitivity Analysis (ESA) method (Ancell and Hakim 2007; Torn and Hakim 2008).

  3. A New Reference for Wind Power Forecasting

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Joensen, Alfred K.; Madsen, Henrik

    1998-01-01

    In recent years some research towards developing forecasting models for wind power or energy has been carried out. In order to evaluate the prediction ability of these models, the forecasts are usually compared with those of the persistence forecast model. As shown in this article, however...

  4. Powering Up With Space-Time Wind Forecasting

    KAUST Repository

    Hering, Amanda S.

    2010-03-01

    The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.

  5. High resolution probabilistic forecasting for wind energy applications.

    Science.gov (United States)

    Courtney, J.; Sweeney, C.; Lynch, P.

    2012-04-01

    This project aims to produce the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) and produce a more accurate forecast than the ensemble mean. The 51 members are clustered to 8 weighted representative members (RMs) using a clustering technique. The 8 RMs are chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. Numerical weather prediction is far from perfect and each of the ensemble member forecasts contains errors, both systematic and chaotic. Systematic errors can be minimized with statistical post-processing. We apply four adaptive post-processing methods to each forecast which require only a short training period. The weighted ensemble mean of the post-processed ensembles is used as the input to a Bayesian Model Averaging (BMA) system. Each ensemble forecast probability density function (PDF) is weighted based on how well it has performed over a training period. The weighted PDFs are then summed to form the BMA PDF which represents the probability of all possible wind speeds and has been proven to outperform the ensemble mean. We present a detailed description of the above process and detail some preliminary results.

  6. Characterization of wind speed data according to wind direction

    Energy Technology Data Exchange (ETDEWEB)

    Torres, J.L.; Garcia, A.; Prieto, E. [Universidad Publica de Navarra, Pamplona (Spain). Dpto. Proyectos e Ingenieria Rural; Francisco, A. De [Universidad Politecnica de Madrid (Spain). Dpto. de Ingenieria Forestal

    1999-05-01

    Knowledge of the wind speed distribution and the most frequent wind directions is important when choosing wind turbines and when locating them. For this reason wind evaluation and characterization are important when forecasting output power. The data used here were collected from eleven meteorological stations distributed in Navarre, Spain. We obtained data for the period extending from 1992 to 1995, with each datum encompassing 10 minutes of time. Wind speed data of each station were gathered in eight directional sectors, each one extended over 45 degrees according to the direction from which the wind blows. The stations were grouped in two blocks: those under the influence of the Ebro valley and those in mountainous areas. For each group the Weibull parameters were estimated, (according to the Weibull probability paper because the Weibull distribution gives the best fit in this region). Kurtosis and skewness coefficients were estimated as well. The Weibull parameters, especially the scale parameter c, depend strongly on the direction considered, and both Weibull parameters show an increasing trend as the direction considered moves to the more dominant direction, while both kurtosis and skewness show a corresponding decreasing trend. (author)

  7. Spatial and temporal patterns of global onshore wind speed distribution

    International Nuclear Information System (INIS)

    Zhou, Yuyu; Smith, Steven J

    2013-01-01

    Wind power, a renewable energy source, can play an important role in electrical energy generation. Information regarding wind energy potential is important both for energy related modeling and for decision-making in the policy community. While wind speed datasets with high spatial and temporal resolution are often ultimately used for detailed planning, simpler assumptions are often used in analysis work. An accurate representation of the wind speed frequency distribution is needed in order to properly characterize wind energy potential. Using a power density method, this study estimated global variation in wind parameters as fitted to a Weibull density function using NCEP/climate forecast system reanalysis (CFSR) data over land areas. The Weibull distribution performs well in fitting the time series wind speed data at most locations according to R 2 , root mean square error, and power density error. The wind speed frequency distribution, as represented by the Weibull k parameter, exhibits a large amount of spatial variation, a regionally varying amount of seasonal variation, and relatively low decadal variation. We also analyzed the potential error in wind power estimation when a commonly assumed Rayleigh distribution (Weibull k = 2) is used. We find that the assumption of the same Weibull parameter across large regions can result in non-negligible errors. While large-scale wind speed data are often presented in the form of mean wind speeds, these results highlight the need to also provide information on the wind speed frequency distribution. (letter)

  8. “Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

    Directory of Open Access Journals (Sweden)

    Dunnan Liu

    2015-01-01

    Full Text Available As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called “garbage in, garbage out,” which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of “section to point” correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.

  9. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le

    2014-01-01

    We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

  10. Improving Wind-Ramp Forecasts in the Stable Boundary Layer

    Science.gov (United States)

    Jahn, David E.; Takle, Eugene S.; Gallus, William A.

    2017-06-01

    The viability of wind-energy generation is dependent on highly accurate numerical wind forecasts, which are impeded by inaccuracies in model representation of boundary-layer processes. This study revisits the basic theory of the Mellor, Yamada, Nakanishi, and Niino (MYNN) planetary boundary-layer parametrization scheme, focusing on the onset of wind-ramp events related to nocturnal low-level jets. Modifications to the MYNN scheme include: (1) calculation of new closure parameters that determine the relative effects of turbulent energy production, dissipation, and redistribution; (2) enhanced mixing in the stable boundary layer when the mean wind speed exceeds a specified threshold; (3) explicit accounting of turbulent potential energy in the energy budget. A mesoscale model is used to generate short-term (24 h) wind forecasts for a set of 15 cases from both the U.S.A. and Germany. Results show that the new set of closure parameters provides a marked forecast improvement only when used in conjunction with the new mixing length formulation and only for cases that are originally under- or over-forecast (10 of the 15 cases). For these cases, the mean absolute error (MAE) of wind forecasts at turbine-hub height is reduced on average by 17%. A reduction in MAE values on average by 26% is realized for these same cases when accounting for the turbulent potential energy together with the new mixing length. This last method results in an average reduction by at least 13% in MAE values across all 15 cases.

  11. Forecasting volatility of wind power production

    OpenAIRE

    Zhiwei Shen; Matthias Ritter

    2015-01-01

    Abstract: The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switch...

  12. Wind power forecasting: IEA Wind Task 36 & future research issues

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, J.; Frank, Helmut Paul

    2016-01-01

    , MetOffice, met.no, DMI,...), operational forecaster and forecast users.The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely......Bench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented....

  13. Speed control at low wind speeds for a variable speed fixed pitch wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Rosmin, N.; Watson, S.J.; Tompson, M. [Loughborough Univ., Loughborough, Leicestershire (United Kingdom)

    2010-03-09

    The maximum power regulation below rated wind speed is regulated by changing the rotor/generator speed at large frequency range in a fixed pitch, variable speed, stall-regulated wind turbine. In order to capture the power at a maximum value the power coefficient is kept at maximum peak point by maintaining the tip speed ratio at its optimum value. The wind industry is moving from stall regulated fixed speed wind turbines to newer improved innovative versions with better reliability. While a stall regulated fixed pitch wind turbine is among the most cost-effective wind turbine on the market, its problems include noise, severe vibrations, high thrust loads and low power efficiency. Therefore, in order to improve such drawbacks, the rotation of the generator speed is made flexible where the rotation can be controlled in variable speed. This paper discussed the development of a simulation model which represented the behaviour of a stall regulated variable speed wind turbine at low wind speed control region by using the closed loop scalar control with adjustable speed drive. The paper provided a description of each sub-model in the wind turbine system and described the scalar control of the induction machine. It was concluded that by using a constant voltage/frequency ratio of the generator's stator side control, the generator speed could be regulated and the generator torque could be controlled to ensure the power coefficient could be maintained close to its maximum value. 38 refs., 1 tab., 10 figs.

  14. EU-NORSEWIND - Delivering Offshore Wind Speed Data

    DEFF Research Database (Denmark)

    Oldroyd, Andy; Hasager, Charlotte Bay; Stickland, M.T.

    of the offshore wind climatology and likely wind resource. As the wind industry starts to look in detail at the wind regime offshore, the need for more physical data becomes apparent. As well as the normal AEP requirements for project finance, baseline data is required in order to better understand the local...... of large scale wind farms in relatively concentrated geographical areas. NORSEWInD has a clear remit, the delivery of offshore wind speed data at a nominal project hub height acquired in offshore locations. The project will use a multi-instrument approach, combining mast technology, LiDAR remote sensing...... and satellite based observations to compile a large and novel wind speed dataset suitable for use in the wind industry. The data will also feed into key areas such as forecasting and MESOSCALE modelling improvements. The result is a large database accessible via a web based interface utilising GIS...

  15. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

    OpenAIRE

    Wen-Yeau Chang

    2013-01-01

    High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...

  16. Use of ground-based wind profiles in mesoscale forecasting

    Science.gov (United States)

    Schlatter, Thomas W.

    1985-01-01

    A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

  17. A survey on wind power ramp forecasting.

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-02-23

    The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

  18. Early warnings of extreme winds using the ECMWF Extreme Forecast Index

    OpenAIRE

    Petroliagis, Thomas I.; Pinson, Pierre

    2014-01-01

    The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked...

  19. Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Barrett, Joe, III; Short, David; Roeder, William

    2008-01-01

    The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of

  20. Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

    Directory of Open Access Journals (Sweden)

    Julia Moemken

    2016-03-01

    Full Text Available Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’ decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM, and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction

  1. Short-term wind speed prediction based on the wavelet transformation and Adaboost neural network

    Science.gov (United States)

    Hai, Zhou; Xiang, Zhu; Haijian, Shao; Ji, Wu

    2018-03-01

    The operation of the power grid will be affected inevitably with the increasing scale of wind farm due to the inherent randomness and uncertainty, so the accurate wind speed forecasting is critical for the stability of the grid operation. Typically, the traditional forecasting method does not take into account the frequency characteristics of wind speed, which cannot reflect the nature of the wind speed signal changes result from the low generality ability of the model structure. AdaBoost neural network in combination with the multi-resolution and multi-scale decomposition of wind speed is proposed to design the model structure in order to improve the forecasting accuracy and generality ability. The experimental evaluation using the data from a real wind farm in Jiangsu province is given to demonstrate the proposed strategy can improve the robust and accuracy of the forecasted variable.

  2. Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

    Science.gov (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

    2015-01-01

    This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

  3. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  4. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, J.; Hodge, B. M.

    2014-04-01

    This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

  5. Solar and wind forecasting by NARX neural networks

    Directory of Open Access Journals (Sweden)

    Di Piazza Annalisa

    2016-01-01

    Full Text Available The nonlinear autoregressive network with exogenous input (NARX is used to perform hourly solar irradiation and wind speed forecasting, according to a multi-step ahead approach. Temperature has been considered as the exogenous variable. The NARX topology selection is supported by a combined use of two techniques: (1 a genetic algorithm (GA-based optimization technique and (2 a method that determines the optimal network architecture by pruning (optimal brain surgeon (OBS strategy. The considered variables are observed at hourly scale in a seven year dataset and the forecasting is done for several time horizons in the range from 8 to 24 h ahead.

  6. Time-consistent calibration of short-term regional wind power ensemble forecasts

    Directory of Open Access Journals (Sweden)

    Stephan Späth

    2015-04-01

    Full Text Available With increasing wind power capacity, accurate uncertainty forecasts get more and more important for grid integration. The uncertainty of forecasts can be quantified by ensemble forecasts. We use ensemble forecasts from the COSMO-DE EPS to generate short-term ensemble forecasts of regionally aggregated wind power. The wind power forecasts are generated by an optimised regional power curve model that is based on minimum score estimation and leads to wind power forecasts with small deterministic errors. Remaining bias and dispersion errors in the wind power forecasts are removed by statistical post-processing (also called calibration with ensemble model output statistics and the temporal rank correlation of the raw ensemble is maintained by ensemble copula coupling. The verification of raw and calibrated ensembles shows both strong improvements by calibration and the benefit of ensuring time consistency with ensemble copula coupling. The improvements are indicated by the multivariate energy score as well as in a proposed univariate verification approach that is based on integrated wind power forecast and measurement trajectories. Slight deficits in time consistency of the forecasts remain because the theoretical assumptions of ensemble copula coupling are not always fulfilled as the COSMO-DE EPS is based on distinguishable ensemble members. The more training days are used for calibration against measurements of regionally aggregated wind power, the lower is the improvement by calibration which contradicts former results for different variables like wind speed.

  7. Fractal dimension of wind speed time series

    International Nuclear Information System (INIS)

    Chang, Tian-Pau; Ko, Hong-Hsi; Liu, Feng-Jiao; Chen, Pai-Hsun; Chang, Ying-Pin; Liang, Ying-Hsin; Jang, Horng-Yuan; Lin, Tsung-Chi; Chen, Yi-Hwa

    2012-01-01

    Highlights: ► Fractal dimension of wind speeds in Taiwan is studied considering climate factors. ► Relevant algorithms for the calculation of fractal dimension are presented graphically. ► Fractal dimension reveals negative correlation with mean wind speed. ► Fractal dimension is not lower even wind distribution is well described by Weibull pdf. - Abstract: The fluctuation of wind speed within a specific time period affects a lot the energy conversion rate of wind turbine. In this paper, the concept of fractal dimension in chaos theory is applied to investigate wind speed characterizations; numerical algorithms for the calculation of the fractal dimension are presented graphically. Wind data selected is observed at three wind farms experiencing different climatic conditions from 2006 to 2008 in Taiwan, where wind speed distribution can be properly classified to high wind season from October to March and low wind season from April to September. The variations of fractal dimensions among different wind farms are analyzed from the viewpoint of climatic conditions. The results show that the wind speeds studied are characterized by medium to high values of fractal dimension; the annual dimension values lie between 1.61 and 1.66. Because of monsoon factor, the fluctuation of wind speed during high wind months is not as significant as that during low wind months; the value of fractal dimension reveals negative correlation with that of mean wind speed, irrespective of wind farm considered. For a location where the wind distribution is well described by Weibull function, its fractal dimension is not necessarily lower. These findings are useful to wind analysis.

  8. Numerical forecast test on local wind fields at Qinshan Nuclear Power Plant

    International Nuclear Information System (INIS)

    Chen Xiaoqiu

    2005-01-01

    Non-hydrostatic, full compressible atmospheric dynamics model is applied to perform numerical forecast test on local wind fields at Qinshan nuclear power plant, and prognostic data are compared with observed data for wind fields. The results show that the prognostic of wind speeds is better than that of wind directions as compared with observed results. As the whole, the results of prognostic wind field are consistent with meteorological observation data, 54% of wind speeds are within a factor of 1.5, about 61% of the deviation of wind direction within the 1.5 azimuth (≤33.75 degrees) in the first six hours. (authors)

  9. Wind speed dynamical model in a wind farm

    DEFF Research Database (Denmark)

    Soleimanzadeh, Maryam; Wisniewski, Rafal

    2010-01-01

    , the dynamic model for wind flow will be established. The state space variables are determined based on a fine mesh defined for the farm. The end goal of this method is to assist the development of a dynamical model of a wind farm that can be engaged for better wind farm control strategies.......This paper presents a model for wind speed in a wind farm. The basic purpose of the paper is to calculate approximately the wind speed in the vicinity of each wind turbine in a farm. In this regard the governing equations of flow will be solved for the whole wind farm. In ideal circumstances...

  10. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    Energy Technology Data Exchange (ETDEWEB)

    Wilczak, James M. [NOAA, Boulder, CO (United States); Finley, Cathy [WindLogics, Inc., St. Paul, MN (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States); Cline, Joel [USDOE Office of Energy Efficiency and Renewable Energy, Washington, DC (United States); Bianco, L. [Univ. of Colorado, Boulder, CO (United States); Olson, J. [Univ. of Colorado, Boulder, CO (United States); Djalaova, I. [Univ. of Colorado, Boulder, CO (United States); Sheridan, L. [WindLogics, Inc., St. Paul, MN (United States); Ahlstrom, M. [WindLogics, Inc., St. Paul, MN (United States); Manobianco, J. [Meso, Inc., Troy, NY (United States); Zack, J. [Meso, Inc., Troy, NY (United States); Carley, J. [National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States); Benjamin, S. [NOAA, Boulder, CO (United States); Coulter, R. L. [Argonne National Lab. (ANL), Lemont, IL (United States); Berg, Larry K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Mirocha, Jeff D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Clawson, K. [National Oceanic and Atmospheric Administration (NOAA), Idaho Falls, ID (United States); Natenberg, E. [Meso, Inc., Troy, NY (United States); Marquis, M. [NOAA, Boulder, CO (United States)

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  11. When real life wind speed exceeds design wind assumptions

    Energy Technology Data Exchange (ETDEWEB)

    Winther-Jensen, M.; Joergensen, E.R. [Risoe National Lab., Roskilde (Denmark)

    1999-03-01

    Most modern wind turbines are designed according to a standard or a set of standards to withstand the design loads with a defined survival probability. Mainly the loads are given by the wind conditions on the site defining the `design wind speeds`, normally including extreme wind speeds given as an average and a peak value. The extreme wind speeds are normally (e.g. in the upcoming IEC standard for wind turbine safety) defined as having a 50-year recurrence period. But what happens when the 100 or 10,000 year wind situation hits a wind turbine? Results on wind turbines of wind speeds higher than the extreme design wind speeds are presented based on experiences especially from the State of Gujarat in India. A description of the normal approach of designing wind turbines in accordance with the standards in briefly given in this paper with special focus on limitations and built-in safety levels. Based on that, other possibilities than just accepting damages on wind turbines exposed for higher than design wind speeds are mentioned and discussed. The presentation does not intend to give the final answer to this problem but is meant as an input to further investigations and discussions. (au)

  12. Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

    Science.gov (United States)

    Delle Monache, L.; Shahriari, M.; Cervone, G.

    2017-12-01

    We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

  13. Modeling and forecasting of wind power generation - Regime-switching approaches

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien

    of more renewable energy into power systems since these systems are subjected to maintain a strict balance between electricity consumption and production, at any time. For this purpose, wind power forecasts offer an essential support to power system operators. In particular, there is a growing demand...... of high and low variability. They also yield substantial gains in probabilistic forecast accuracy for lead times of a few minutes. However, these models only integrate historical and local measurements of wind power and thus have a limited ability for notifying regime changes for larger lead times....... For that purpose, there is a long tradition in using meteorological forecasts of wind speed and direction that are converted into wind power forecasts. Nevertheless, meteorological forecasts are not informative on the intra-hour wind variability and thus cannot be used in the present context focusing on temporal...

  14. Offshore wind speed and wind power characteristics for ten ...

    Indian Academy of Sciences (India)

    This paper utilizes wind speed data measured at 3 and 10 m above water surface level using buoys at 10 stations in Ionian and Aegean Seas to understand the behaviour of wind and thereafter energy yield at these stations using 5 MW rated power offshore wind turbine. With wind power densities of 971 and 693 W/m2 at ...

  15. Artificial intelligence to predict short-term wind speed

    Energy Technology Data Exchange (ETDEWEB)

    Pinto, Tiago; Soares, Joao; Ramos, Sergio; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - ISEP

    2012-07-01

    The use of renewable energy is increasing exponentially in many countries due to the introduction of new energy and environmental policies. Thus, the focus on energy and on the environment makes the efficient integration of renewable energy into the electric power system extremely important. Several European countries have been seeing a high penetration of wind power, representing, gradually, a significant penetration on electricity generation. The introduction of wind power in the network power system causes new challenges for the power system operator due to the variability and uncertainty in weather conditions and, consequently, in the wind power generation. As result, the scheduling dispatch has a significantly portion of uncertainty. In order to deal with the uncertainty in wind power and, with that, introduce improvements in the power system operator efficiency, the wind power forecasting may reveal as a useful tool. This paper proposes a data-mining-based methodology to forecast wind speed. This method is based on the use of data mining techniques applied to a real database of historical wind data. The paper includes a case study based on a real database regarding the last three years to predict wind speed at 5 minute intervals. (orig.)

  16. Hourly wind speed analysis in Sicily

    Energy Technology Data Exchange (ETDEWEB)

    Bivona, S.; Leone, C. [Palermo Univ., Dip di Fisica e Technologie Relative, Palermo (Italy); Burlon, R. [Palermo Univ., Dip. di Ingegnaria Nucleare, Palermo (Italy)

    2003-07-01

    The hourly average wind speed data recorded by CNMCA (Centro Nazionale di Meteorologia e Climatologia Aeronautica) have been used to study the statistical properties of the wind speed at nine locations on Sicily. By grouping the observations month by month, we show that the hourly average wind speed, with calms omitted, is represented by a Weibull function. The suitability of the distribution is judged by the discrepancies between the observed and calculated values of the monthly average wind speed and of the standard deviation. (Author)

  17. Scour Forecasting for Offshore Wind Parks

    DEFF Research Database (Denmark)

    Hartvig, Peres Akrawi

    , scour forecasts facilitate the comparison between a scour design based on either deployment of scour-protection or enhanced structural design. The broad goal is to develop a method that produces accurate scour forecasts for offshore wind parks. The present research investigates more specifically which...... two legacies that deal with these two research questions in dialectical ways. The first legacy is a framework for the scour geometry based on epistemological considerations, theoretical concepts and model scale experiments. Relevant parameters are reviewed, defined and discussed. The combined use...

  18. Sharing wind power forecasts in electricity markets: A numerical analysis

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

    2016-01-01

    In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisi...

  19. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

    Directory of Open Access Journals (Sweden)

    Wen-Yeau Chang

    2013-09-01

    Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

  20. Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

    OpenAIRE

    Ju Feng; Wen Zhong Shen

    2015-01-01

    Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distributions of wind speed and wind direction, which is based on the parameters of sector-wise Weibull distributions and interpolations between direction sectors. It is applied to the wind measurement data a...

  1. A Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Crawford, Winifred; Roeder, William

    2008-01-01

    This conference abstract describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violatioas.The tool will include climatologies of the 5-minute mean end peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  2. Different Models for Forecasting Wind Power Generation: Case Study

    Directory of Open Access Journals (Sweden)

    David Barbosa de Alencar

    2017-11-01

    Full Text Available Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA, Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.

  3. Critical wind speed at which trees break

    Science.gov (United States)

    Virot, E.; Ponomarenko, A.; Dehandschoewercker, É.; Quéré, D.; Clanet, C.

    2016-02-01

    Data from storms suggest that the critical wind speed at which trees break is constant (≃42 m /s ), regardless of tree characteristics. We question the physical origin of this observation both experimentally and theoretically. By combining Hooke's law, Griffith's criterion, and tree allometry, we show that the critical wind speed indeed hardly depends on the height, diameter, and elastic properties of trees.

  4. Seasonal forecast verification of extreme events for the wind energy sector

    Science.gov (United States)

    Lee, Doo Young; González-Reviriego, Nube; Torralba, Veronica; Cortesi, Nicola; Marcos, Raül; Doblas-Reyes, Francisco

    2017-04-01

    Severe and extreme winds and related destructive wind storms are the second highest cause of global natural catastrophe insurance losses after hurricanes. For this reason, a more accurate assessment of the probability of occurrence of these severe wind speed events is necessary to increase the protection and to minimize the risk of unexpected energy network unbalance. In this study, we focus on the evaluation of the ability of the global seasonal climate prediction systems in forecasting extreme wind speeds. The climate forecast systems employed are the ECMWF seasonal forecast system 4 (ECMWF-S4) and Meteo-France's Systems 4 (METFR-S4). We consider extreme events based on the upper (90th percentile) or lower (10th percentile) thresholds of 6-hourly 10m wind speed within a month. Then 3-month averages of those events have been analyzed at 0-4 months lead time for the May and November start dates during the period 1991-2012. We evaluate the performance of the seasonal climate prediction systems when predicting extreme wind speed at different forecast horizons, by means of deterministic and probabilistic skill measures, such as the temporal correlation coefficient (TCC) and the fair ranked probability skill Score (FRPSS). At the seasonal time scale, this investigation is a first step for providing better climate information to characterize the low and high wind speeds in a particular location.

  5. Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

    DEFF Research Database (Denmark)

    Tastu, Julija; Pinson, Pierre; Kotwa, Ewelina

    2011-01-01

    for the spatio‐temporal dependencies observed in the wind generation field. However, it is intuitively expected that, owing to the inertia of meteorological forecasting systems, a forecast error made at a given point in space and time will be related to forecast errors at other points in space in the following...... of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power...... period. The existence of such underlying correlation patterns is demonstrated and analyzed in this paper, considering the case‐study of western Denmark. The effects of prevailing wind speed and direction on autocorrelation and cross‐correlation patterns are thoroughly described. For a flat terrain region...

  6. Wind-Ramp-Forecast Sensitivity to Closure Parameters in a Boundary-Layer Parametrization Scheme

    Science.gov (United States)

    Jahn, David E.; Takle, Eugene S.; Gallus, William A.

    2017-09-01

    Wind ramps are relatively large changes in wind speed over a period of a few hours and present a challenge for electric utilities to balance power generation and load. Failures of boundary-layer parametrization schemes to represent physical processes limit the ability of numerical models to forecast wind ramps, especially in a stable boundary layer. Herein, the eight "closure parameters" of a widely used boundary-layer parameterization scheme are subject to sensitivity tests for a set of wind-ramp cases. A marked sensitivity of forecast wind speed to closure-parameter values is observed primarily for three parameters that influence in the closure equations the depth of turbulent mixing, dissipation, and the transfer of kinetic energy from the mean to the turbulent flow. Reducing the value of these parameters independently by 25% or by 50% reduces the overall average in forecast wind-speed errors by at least 24% for the first two parameters and increases average forecast error by at least 63% for the third parameter. Doubling any of these three parameters increases average forecast error by at least 67%. Such forecast sensitivity to closure parameter values provides motivation to explore alternative values in the context of a stable boundary layer.

  7. Detecting, categorizing and forecasting large romps in wind farm power output using meteorological observations and WPPT

    DEFF Research Database (Denmark)

    Cutler, N.; Kay, M.; Jacka, K.

    2007-01-01

    The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind form. This article analyses the general performance of WPPT as well as its performance during large romps...... (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real-time wind power observations to make hourly...... forecasts of the wind farm power output. The general performances of MesoLAPS and WPPTore evaluated over I year using the root mean square error (RMSE). The errors are significantly lower than for basic benchmark forecasts but higher than for many other WPPT installations, where the site conditions...

  8. Direct Interval Forecasting of Wind Power

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2013-01-01

    This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness, wit......, without the prior knowledge of forecasting errors. The proposed approach has been proved to be highly efficient and reliable through preliminary case studies using real-world wind farm data, indicating a high potential of practical application.......This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness...

  9. LIDAR Wind Speed Measurements of Evolving Wind Fields

    Energy Technology Data Exchange (ETDEWEB)

    Simley, E.; Pao, L. Y.

    2012-07-01

    Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylor's frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor's hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. LIDAR measurements are also evaluated with a large eddy simulation of a stable boundary layer provided by the National Center for Atmospheric Research. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.

  10. Using ensemble NWP wind power forecasts to improve national power system management

    Science.gov (United States)

    Cannon, D.; Brayshaw, D.; Methven, J.; Coker, P.; Lenaghan, D.

    2014-12-01

    National power systems are becoming increasingly sensitive to atmospheric variability as generation from wind (and other renewables) increases. As such, the days-ahead predictability of wind power has significant implications for power system management. At this time horizon, power system operators plan transmission line outages for maintenance. In addition, forecast users begin to form backup strategies to account for the uncertainty in wind power predictions. Under-estimating this uncertainty could result in a failure to meet system security standards, or in the worst instance, a shortfall in total electricity supply. On the other hand, overly conservative assumptions about the forecast uncertainty incur costs associated with the unnecessary holding of reserve power. Using the power system of Great Britain (GB) as an example, we construct time series of GB-total wind power output using wind speeds from either reanalyses or global weather forecasts. To validate the accuracy of these data sets, wind power reconstructions using reanalyses and forecast analyses over a recent period are compared to measured GB-total power output. The results are found to be highly correlated on time scales greater than around 6 hours. Results are presented using ensemble wind power forecasts from several national and international forecast centres (obtained through TIGGE). Firstly, the skill with which global ensemble forecasts can represent the uncertainty in the GB-total power output at up to 10 days ahead is quantified. Following this, novel ensemble forecast metrics are developed to improve estimates of forecast uncertainty within the context of power system operations, thus enabling the development of more cost effective strategies. Finally, the predictability of extreme events such as prolonged low wind periods or rapid changes in wind power output are examined in detail. These events, if poorly forecast, induce high stress scenarios that could threaten the security of the power

  11. Short-term wind power combined forecasting based on error forecast correction

    International Nuclear Information System (INIS)

    Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

    2016-01-01

    Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

  12. Detecting, categorizing and forecasting large romps in wind farm power output using meteorological observations and WPPT

    DEFF Research Database (Denmark)

    Cutler, N.; Kay, M.; Jacka, K.

    2007-01-01

    The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind form. This article analyses the general performance of WPPT as well as its performance during large romps...... are not as complicated as Woolnorth Bluff Point. Large ramps are considered critical events for a wind power forecast for energy trading as well as managing power system security. A methodology is developed to detect large ramp events in the wind farm power data. Forty-one large ramp events are detected over I year...... (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real-time wind power observations to make hourly...

  13. Effective Short-term Forecasting of Wind Farms Power

    Directory of Open Access Journals (Sweden)

    Elżbieta Bogalecka

    2015-09-01

    Full Text Available Forecasting a specific wind farm’s (WF generation capacity within a 24 hour perspective requires both a reliable forecast of wind, as well as supporting tools. This tool is a dedicated model of wind farm power. This model should include not only general rules of wind to mechanical energy conversion, but also the farm’s specific features. There are many factors that influence a farm’s generation capacity, and any forecast of it, even with an accurate weather forecast, carries error. This paper presents analytical, statistical, and neuron models of wind farm power. The study is based on data from a real wind farm. Most attention is paid to the neuron models, due to a neuron network’s capability to restore farm-specific details. The research aims to answer the headline question: whether and to what extent a wind farm’s power can be forecast short-term?

  14. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, B. M.; Lew, D.; Milligan, M.; Holttinen, H.; Sillanpaa, S.; Gomez-Lazaro, E.; Scharff, R.; Soder, L.; Larsen, X. G.; Giebel, G.; Flynn, D.; Dobschinski, J.

    2012-09-01

    Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

  15. Wind Power Forecasting Error Distributions: An International Comparison

    DEFF Research Database (Denmark)

    Hodge, Bri-Mathias; Lew, Debra; Milligan, Michael

    2012-01-01

    Wind power forecasting is essential for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that may occur is a critical factor for system operation functions, such as the setting of operating reserve...... levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations....

  16. Low wind speed wind turbine in DIY version

    OpenAIRE

    Van den Bossche, Alex

    2013-01-01

    Wind energy has still a place, as it can generate power in the winter, when less sun is available and when the wind speed is higher. This paper proposes a solution for low cost blades from a polyethylene pipe (PE) and a low cost electric bike generator, which is possible to realize by a do it yourself (DIY) person. It is intended as low wind speed wind turbine (LWWT). The design is rather optimized towards a low cost/swept area, rather than the cost/nominal power. It uses a variant on the fur...

  17. Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

    Science.gov (United States)

    Moemken, Julia; Reyers, Mark; Buldmann, Benjamin; Pinto, Joaquim G.

    2016-04-01

    Regional climate predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy, and society. In this context, decadal predictions are of particular interest for the development of renewable energies such as wind energy. The present study examines the decadal predictability of regional scale wind speed and wind energy potentials in the framework of the MiKlip consortium ("Mittelfristige Klimaprognosen"; www.fona-miklip.de). This consortium aims to develop a model system based on the Max-Planck-Institute Earth System Model (MPI-ESM) that can provide skilful decadal predictions on regional and global scales. Three generations of the decadal prediction system, which differ primarily in their ocean initialisation, are analysed here. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess different skill scores for 10m wind speeds and wind energy output (Eout) over Central Europe, with special focus given to Germany. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation of the global datasets. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. The forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skill of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer, and persist longest in autumn. A large-scale westerly

  18. Low Speed Wind Tunnel Facility (LSWTF)

    Data.gov (United States)

    Federal Laboratory Consortium — Description: This facility consists of a large-scale, low-speed open-loop induction wind tunnel which has been modified to house a linear turbine cascade. A 125-hp...

  19. SSMI Wind Speed Climatology of the Time of Monsoon Wind Offset in the Western Arabian Sea

    Science.gov (United States)

    Halpern, David

    2000-01-01

    Forecasting the time of onset of monsoon wind in the western Arabian Sea, which is believed to precede the onset of rainfall along the west coast of India, is an important unsolved problem. Prior to measurements of the surface wind field by satellite, there was an absence of suitable surface wind observations. NASA scatterometer (NSCAT) surface wind vectors revealed that the time of the 1997 onset of 12 m/s southwest monsoon wind speeds in the western Arabian Sea preceded the onset of monsoon rainfall in Goa, India, by 3 - 4 days. Wind speed and direction data were necessary to establish a dynamical mechanism between times of onset of 12 m/s wind speed off Somalia and rainfall in Goa. Except for NSCAT, no satellite scatterometer wind product recorded adequately sampled 2-day 1deg x 1deg averaged wind vectors, which are the required space and time scales, to examine the wind-rain relationship in other years. However, the greater-than-95% steadiness of summer monsoon winds allows an opportunity to use satellite measurements of surface wind speed. The Special Sensor Microwave Imager (SSMI) recorded surface wind speed with adequate sampling to produce a 1-day, 1deg x 1deg data product during 1988 - 1998. SSMI data had been uniformly processed throughout the period. Times of onset of 12 m/s wind speed off Somalia determined with the SSMI data set were 21 May 1988, 24 May 1989, 17 May 1990, 28 May 1991, 8 June 1992, 28 May 1993, 30 May 1994, 7 June 1995, 29 May 1996, 12 June 1997, and 15 May 1998. Uncertainty of the 1992 and 1996 times of onset were increased because of the absence of SSMI data on 6 and 7 June 1992 and on 30 May 1996. Correlations of timing of monsoon wind onset with El Nino will be described. Variability of the time difference between times of onset of 12 m/s wind speed and Goa rainfall will be discussed. At the time of submission of the abstract, the Goa rainfall data have not arrived from the India Meteorological Department.

  20. Virtual inertia for variable speed wind turbines

    DEFF Research Database (Denmark)

    Zeni, Lorenzo; Rudolph, Andreas Jakob; Münster-Swendsen, Janus

    2013-01-01

    Inertia provision for frequency control is among the ancillary services that different national grid codes will likely require to be provided by future wind turbines. The aim of this paper is analysing how the inertia response support from a variable speed wind turbine (VSWT) to the primary...

  1. Plant gas exchange at high wind speeds

    Energy Technology Data Exchange (ETDEWEB)

    Caldwell, M.M.

    1970-01-01

    High altitude Rhododendron ferrugineum L. and Pinus cembra L. seedlings were exposed to winds at 15 meters per second for 24-hour periods. Wind-sensitive stomata of Rhododendron seedlings immediately initiated a closing response which resulted in decreased photosynthesis and an even greater reduction in transpiration. Stomatal aperture and transpiration rates of P. cembra were only slightly reduced by high speed winds. However, photosynthesis was substantially reduced because of changes in needle display to available irradiation. 17 references, 3 figures.

  2. Plant gas exchange at high wind speeds.

    Science.gov (United States)

    Caldwell, M M

    1970-10-01

    High altitude Rhododendron ferrugineum L. and Pinus cembra L. seedlings were exposed to winds at 15 meters per second for 24-hour periods. Wind-sensitive stomata of Rhododendron seedlings immediately initiated a closing response which resulted in decreased photosynthesis and an even greater reduction in transpiration. Stomatal aperture and transpiration rates of P. cembra were only slightly reduced by high speed winds. However, photosynthesis was substantially reduced because of changes in needle display to available irradiation.

  3. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao

    2016-01-01

    The uncertainty of wind power generation imposes significant challenges to optimal operation and control of electricity networks with increasing wind power penetration. To effectively address the uncertainties in wind power forecasts, probabilistic forecasts that can quantify the associated...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

  4. Forecasting wind-driven wildfires using an inverse modelling approach

    Directory of Open Access Journals (Sweden)

    O. Rios

    2014-06-01

    Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

  5. Comparison of measured and simulated wind speed data in the North Atlantic

    Energy Technology Data Exchange (ETDEWEB)

    Winterfeldt, J. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Kuestenforschung

    2008-11-06

    A systematic investigation and comparison of near-surface marine wind speed obtained from in situ and satellite observations, atmospheric reanalyses and regional atmospheric hindcasts with reanalysis driven regional climate models (RCMs) is presented for the eastern North Atlantic and the North Sea. Wind speed retrievals from two remote sensing data sets, namely QuikSCAT and the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) data set, are found to give good representation of observed near-surface wind speed. The value of the root mean squared error (RMSE) for all co-located HOAPS and in situ wind speed data is 2 m/s, while it is 1.8 m/s for QuikSCAT demonstrating that QuikSCAT's mission requirement of providing wind speed with an RMSE of 2 m/s is met for the eastern North Atlantic and the North Sea. QuikSCAT shows a slightly better agreement with observed instantaneous wind speed and its frequency distribution than HOAPS. In contrast, HOAPS wind speed is available for a much longer period and is therefore the more suitable product for climatic studies or investigations of trends in wind speed. The capability of two state-of-the-art RCMs (with and without spectral nudging applied) to add value for surface marine wind fields in comparison to the reanalysis wind speed forcing is assessed by the comparison with in situ wind speed observations in the eastern North Atlantic in 1998. The comparison of the 10 m wind speed forecasts from the NCEP/NCAR and NCEP/DOE-II reanalyses with in-situ observations demonstrates the implausibility of the latter forecast resulting in its non-consideration in the added value assessment. The added value is investigated for instantaneous wind speeds (relevant for case studies) and their frequency distribution (relevant for e.g., extreme value statistics and estimations of wind potential). The observations are discriminated into groups according to their proximity to land and assimilation status, meaning whether

  6. Aggregated wind power generation probabilistic forecasting based on particle filter

    International Nuclear Information System (INIS)

    Li, Pai; Guan, Xiaohong; Wu, Jiang

    2015-01-01

    Highlights: • A new method for probabilistic forecasting of aggregated wind power generation. • A dynamic system is established based on a numerical weather prediction model. • The new method handles the non-Gaussian and time-varying wind power uncertainties. • Particle filter is applied to forecast predictive densities of wind generation. - Abstract: Probability distribution of aggregated wind power generation in a region is one of important issues for power system daily operation. This paper presents a novel method to forecast the predictive densities of the aggregated wind power generation from several geographically distributed wind farms, considering the non-Gaussian and non-stationary characteristics in wind power uncertainties. Based on a mesoscale numerical weather prediction model, a dynamic system is established to formulate the relationship between the atmospheric and near-surface wind fields of geographically distributed wind farms. A recursively backtracking framework based on the particle filter is applied to estimate the atmospheric state with the near-surface wind power generation measurements, and to forecast the possible samples of the aggregated wind power generation. The predictive densities of the aggregated wind power generation are then estimated based on these predicted samples by a kernel density estimator. In case studies, the new method presented is tested on a 9 wind farms system in Midwestern United States. The testing results that the new method can provide competitive interval forecasts for the aggregated wind power generation with conventional statistical based models, which validates the effectiveness of the new method

  7. ACCUWIND - Accurate wind speed measurements in wind energy - Summary report

    DEFF Research Database (Denmark)

    Friis Pedersen, Troels; Dahlberg, J.-Å.; Cuerva, A.

    2006-01-01

    been used by meteorologists for turbulencemeasurements, but have also found a role on wind turbine nacelles for wind speed and yaw control purposes. The report on cup and sonic anemometry deals with establishment of robustness in assessment and classification by focus on methods and proceduresfor...

  8. Offshore wind speed and wind power characteristics for ten ...

    Indian Academy of Sciences (India)

    At Athos and Mykonos, increasing linear trends were estimated. At all the stations the chosen wind turbine could produce energy for more than 70% of the time. The wind speed distribution was found to be well represented by Weibull parameters obtained using Maximum likelihood method compared to WAsP and Method of ...

  9. Effect of Wind Direction on ENVISAT ASAR Wind Speed Retrieval

    DEFF Research Database (Denmark)

    Takeyama, Yuko; Ohsawa, Teruo; Kozai, Katsutoshi

    2010-01-01

    This paper presents an evaluation of effects of wind directions (NCEP, MANAL, QuickSCAT and WRF) on the sea surface wind speed retrieval from 75 ENVISAT ASAR images with four C-band Geophysical model functions, CMOD4, CMOD_IFR2, CMOD5 and CMOD5N at two target areas, Hiratsuka and Shirahama...

  10. Wind field forecast for accidental release of radiative materials

    International Nuclear Information System (INIS)

    Kang Ling; Chen Jiayi; Cai Xuhui

    2003-01-01

    A meso-scale wind field forecast model was designed for emergency environmental assessment in case of accidental release of radiative materials from a nuclear power station. Actual practice of the model showed that it runs fast, has wind field prediction function, and the result given is accurate. With meteorological data collected from weather stations, and pre-treated by a wind field diagnostic model, the initial wind fields at different times were inputted as initial values and assimilation fields for the forecasting model. The model, in turn, worked out to forecast meso-scale wind field of 24 hours in a horizontal domain of 205 km x 205 km. And then, the diagnostic model was employed again with the forecasting data to obtain more detail information of disturbed wind field by local terrain in a smaller domain of 20.5 km x 20.5 km, of which the nuclear power station is at the center. Using observation data in January, April, July and October of 1996 over the area of Hangzhou Bay, wind fields in these 4 months were simulated by different assimilation time and number of the weather stations for a sensitive test. Results indicated that the method used here has increased accuracy of the forecasted wind fields. And incorporating diagnostic method with the wind field forecast model has greatly increased efficiency of the wind field forecast for the smaller domain. This model and scheme have been used in Environmental Consequence Assessment System of Nuclear Accident in Qinshan Area

  11. Early warnings of extreme winds using the ECMWF Extreme Forecast Index

    DEFF Research Database (Denmark)

    Petroliagis, Thomas I.; Pinson, Pierre

    2014-01-01

    regimes. Overall, it becomes clear that the first indications of an extreme wind event might come from the ECMWF deterministic and/or probabilistic components capturing very intense weather systems (possible windstorms) in the medium term. For early warnings, all available EPS Extreme Forecast Index (EFI......The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic...... stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked to very intense surface cyclonic circulation systems being advected mainly by southwest and northwest flow...

  12. Forecasting wind power production from a wind farm using the RAMS model

    DEFF Research Database (Denmark)

    Tiriolo, L.; Torcasio, R. C.; Montesanti, S.

    2015-01-01

    The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex...... and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution...... of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available...

  13. On probabilistic forecasting of wind power time-series

    DEFF Research Database (Denmark)

    Pinson, Pierre

    Wind power generation is a nonlinear and bounded variable, partly owing to the power curve that converts wind to electric power, and partly owing to the very stochastic nature of wind itself. Predictive densities of wind power generation should account for that effect. Such densities are clearly...... power dynamics. In both cases, the model parameters are adaptively and recursively estimated, time-adaptativity being the result of exponential forgetting of past observations. The probabilistic forecasting methodology is applied at the Horns Rev wind farm in Denmark, for 10-minute ahead probabilistic...... forecasting of wind power generation. Probabilistic forecasts generated from the proposed methodology clearly have higher skill than those obtained from a classical Gaussian assumption about wind power predictive densities. Corresponding point forecasts also exhibit significantly lower error criteria....

  14. Wake flow characteristics at high wind speed

    DEFF Research Database (Denmark)

    Aagaard Madsen, Helge; Larsen, Torben J.; Larsen, Gunner Chr.

    2016-01-01

    Wake flow characteristic at high wind speeds is the main subject of this paper. Although the wake losses decrease at high wind speeds it has been found in a recent study that for multiple wake inflow the increase in loading due to wake effects are substantial even at wind speeds well above rated...... power. In the present study we simulate the wake flow for a row of turbines with the wind aligned with the row using a simplified approach. The velocity deficit, being a function of the thrust coefficient, is simulated based on the BEM solution for wake expansion. An axis-symmetric boundary layer...... equation model (the same as implemented in the DWM model) is subsequently used to develop the deficit down to the next turbine, and then the approach is successively repeated. Simulation results for four different spacing’s in a row with eight turbines show that there are two major flow regimes...

  15. INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

    KAUST Repository

    Elkantassi, Soumaya

    2017-10-03

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  16. Wind speed time series reconstruction using a hybrid neural genetic approach

    Science.gov (United States)

    Rodriguez, H.; Flores, J. J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F.

    2017-11-01

    Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.

  17. Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

    Directory of Open Access Journals (Sweden)

    Aiqing Kang

    2017-01-01

    Full Text Available Hybrid Ensemble Empirical Mode Decomposition (EEMD and Least Square Support Vector Machine (LSSVM is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP, Auto-Regressive Integrated Moving Average (ARIMA, combination of Empirical Mode Decomposition (EMD with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

  18. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

    Science.gov (United States)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

  19. Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

    DEFF Research Database (Denmark)

    Feng, Ju; Shen, Wen Zhong

    2015-01-01

    Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distribu......Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint...... quite well in terms of the coefficient of determination R-2. Then, the best of these joint distributions is used in the layout optimization of the Horns Rev 1 wind farm and the choice of bin sizes for wind speed and wind direction is also investigated. It is found that the choice of bin size for wind...... direction is especially critical for layout optimization and the recommended choice of bin sizes for wind speed and wind direction is finally presented....

  20. Probabilistic forecasting of wind power generation using extreme learning machine

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes...... an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified...... with the best performance. Consequently, a new method for prediction intervals formulation based on theELMand the pairs bootstrap is developed.Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results...

  1. Skill forecasting from ensemble predictions of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

    2009-01-01

    Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

  2. Hydro-Quebec and Environment Canada wind energy forecasting project

    Energy Technology Data Exchange (ETDEWEB)

    Forcione, A.; Roberge, G. [Hydro-Quebec, Saguenay, PQ (Canada). IREQ; Petrucci, F.; Yu, W. [Environment Canada, Gatineau, PQ (Canada)

    2008-07-01

    This presentation outlined a joint wind energy forecasting project currently being conducted by Hydro-Quebec and Environment Canada. The aim of the project is to provide high quality next day hourly forecasts to permit optimal planning and scheduling of wind balancing needs. The quality of next day hourly forecasts depends on the availability of high quality numerical weather prediction modelling output. The model currently being developed by the researchers has been designed to provide 48-hour high resolution, hourly wind forecasts. The Systeme de Provision Eolienne (SPEO) uses mesoscale and microscale operational forecast components from Environment Canada as well as local meteorological observations to provide the wind forecast. Operational forecasts are generated using a global environmental multi-scale model (GEM). Digital elevation models are used to provide high resolution physical data. Case studies of wind forecasts made using the model were provided, as well as flow charts describing a chronology of processes used by the model. It was concluded that the model provides accurate next day wind forecasts. tabs., figs.

  3. Maximum wind speeds and US hurricane losses

    Science.gov (United States)

    Murnane, R. J.; Elsner, J. B.

    2012-08-01

    There is academic, commercial, and public interest in estimating loss from hurricanes striking land and understanding how loss might change as a result of future variations in climate. Here we show that the relationship between wind speed and loss is exponential and that loss increases with wind speed at a rate of 5% per m s-1. The relationship is derived using quantile regression and a data set comprising wind speeds of hurricanes hitting the United States and normalized economic losses. We suggest that the “centercepts” for the different quantiles account for exposure-related factors such as population density, precipitation, and surface roughness, and that once these effects are accounted for, the increase in loss with wind speed is consistent across quantiles. An out-of-sample test of this relationship correctly predicts economic losses from Hurricane Irene in 2011. The exponential relationship suggests that increased wind speeds will produce significantly higher losses; however, increases in exposed property and population are expected to be a more important factor for near future losses.

  4. Wind Energy: Forecasting Challenges for its Operational Management

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2013-01-01

    in a probabilistic framework. Even though,eventually, the forecaster may only communicate single-valued predictions.The existing approaches to wind power forecasting are subsequently described, with focus on single-valued predictions, predictive marginal densities and space-time trajectories. Upcoming challenges...... related to generating improved and new types of forecasts, as well as their verification and value to forecast users, are finally discussed. ½...

  5. A high resolution WRF model for wind energy forecasting

    Science.gov (United States)

    Vincent, Claire Louise; Liu, Yubao

    2010-05-01

    The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the

  6. Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

    Science.gov (United States)

    MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan

    2011-01-01

    The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

  7. Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model

    Science.gov (United States)

    Patricia L. Andrews

    2012-01-01

    Rothermel's surface fire spread model was developed to use a value for the wind speed that affects surface fire, called midflame wind speed. Models have been developed to adjust 20-ft wind speed to midflame wind speed for sheltered and unsheltered surface fuel. In this report, Wind Adjustment Factor (WAF) model equations are given, and the BehavePlus fire modeling...

  8. Doppler Lidar in the Wind Forecast Improvement Projects

    Directory of Open Access Journals (Sweden)

    Pichugina Yelena

    2016-01-01

    Full Text Available This paper will provide an overview of some projects in support of Wind Energy development involving Doppler lidar measurement of wind flow profiles. The high temporal and vertical resolution of these profiles allows the uncertainty of Numerical Weather Prediction models to be evaluated in forecasting dynamic processes and wind flow phenomena in the layer of rotor-blade operation.

  9. Wind power forecasting accuracy and uncertainty in Finland

    Energy Technology Data Exchange (ETDEWEB)

    Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

    2013-04-15

    Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

  10. The economic value of accurate wind power forecasting to utilities

    Energy Technology Data Exchange (ETDEWEB)

    Watson, S.J. [Rutherford Appleton Lab., Oxfordshire (United Kingdom); Giebel, G.; Joensen, A. [Risoe National Lab., Dept. of Wind Energy and Atmospheric Physics, Roskilde (Denmark)

    1999-03-01

    With increasing penetrations of wind power, the need for accurate forecasting is becoming ever more important. Wind power is by its very nature intermittent. For utility schedulers this presents its own problems particularly when the penetration of wind power capacity in a grid reaches a significant level (>20%). However, using accurate forecasts of wind power at wind farm sites, schedulers are able to plan the operation of conventional power capacity to accommodate the fluctuating demands of consumers and wind farm output. The results of a study to assess the value of forecasting at several potential wind farm sites in the UK and in the US state of Iowa using the Reading University/Rutherford Appleton Laboratory National Grid Model (NGM) are presented. The results are assessed for different types of wind power forecasting, namely: persistence, optimised numerical weather prediction or perfect forecasting. In particular, it will shown how the NGM has been used to assess the value of numerical weather prediction forecasts from the Danish Meteorological Institute model, HIRLAM, and the US Nested Grid Model, which have been `site tailored` by the use of the linearized flow model WA{sup s}P and by various Model output Statistics (MOS) and autoregressive techniques. (au)

  11. A Gaussian process regression based hybrid approach for short-term wind speed prediction

    International Nuclear Information System (INIS)

    Zhang, Chi; Wei, Haikun; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian

    2016-01-01

    Highlights: • A novel hybrid approach is proposed for short-term wind speed prediction. • This method combines the parametric AR model with the non-parametric GPR model. • The relative importance of different inputs is considered. • Different types of covariance functions are considered and combined. • It can provide both accurate point forecasts and satisfactory prediction intervals. - Abstract: This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

  12. Peak Wind Forecasts for the Launch-Critical Wind Towers on Kennedy Space Center/Cape Canaveral Air Force Station, Phase IV

    Science.gov (United States)

    Crawford, Winifred

    2011-01-01

    This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds arc an important forecast clement for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to update the statistics in the current peak-wind forecast tool to assist in forecasting LCC violations. The tool includes onshore and offshore flow climatologies of the 5-minute mean and peak winds and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  13. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

    Directory of Open Access Journals (Sweden)

    Dehua Zheng

    2017-12-01

    Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

  14. Wind Speed Measurement by Paper Anemometer

    Science.gov (United States)

    Zhong, Juhua; Cheng, Zhongqi; Guan, Wenchuan

    2011-01-01

    A simple wind speed measurement device, a paper anemometer, is fabricated based on the theory of standing waves. In providing the working profile of the paper anemometer, an experimental device is established, which consists of an anemometer sensor, a sound sensor, a microphone, paper strips, a paper cup, and sonic acquisition software. It shows…

  15. Fiber Laser for Wind Speed Measurements

    DEFF Research Database (Denmark)

    Olesen, Anders Sig

    This PhD thesis evaluates the practical construction and use of a Frequency Stepped Pulse Train modulated coherent Doppler wind lidar (FSPT lidar) for wind speed measurement. The concept of Doppler lidar is introduced as a means to measure line of sight wind speed by the Doppler shift of reflected...... light from aerosols. Central concepts are introduced and developed, i.a. heterodyne detection, carrier-to-noise ratio, probe length, measuring distance, and velocity precision. On this basis the concepts of a FSPT lidar are introduced and its general setup explained. The Lightwave Synthesized Frequency...... Sweeper (LSFS) is introduced and analyzed as a light source for the FSPT lidar. The setup of the LSFS is discussed, and the necessary concepts for modeling and analyzing LSFS noise are developed. The model and measurements are then used to discuss the growth of optical noise in the LSFS and the impact...

  16. TradeWind Deliverable 2.2: Forecast error of aggregated wind power

    DEFF Research Database (Denmark)

    Giebel, Gregor; Sørensen, Poul Ejnar; Holttinen, Hannele

    2007-01-01

    This report is written in fulfilment of Task 2.3 in the TradeWind project (EU sponsored, under the Intelligent Energy Europe initiative): Wind Power Integration and Exchange in the Trans-European Power Market. The Task description is as follows: Task 2.3: Forecast error of aggregated wind power...... Estimates of forecast error of aggregated production for time horizons of intraday and dayahead markets in future will be produced. This will be done by reference to published studies of forecasting for wind generation, and from internal knowledge of WP2 participants. Modelling of wind power fluctuations...

  17. Surface drag effects on simulated wind fields in high-resolution atmospheric forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Kyo Sun; Lim, Jong Myoung; Ji, Young Yong [Environmental Radioactivity Assessment Team,Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Shin, Hye Yum [NOAA/Geophysical Fluid Dynamics Laboratory, Princeton (United States); Hong, Jin Kyu [Yonsei University, Seoul (Korea, Republic of)

    2017-04-15

    It has been reported that the Weather Research and Forecasting (WRF) model generally shows a substantial over prediction bias at low to moderate wind speeds and winds are too geostrophic (Cheng and Steenburgh 2005), which limits the application of WRF model in the area that requires the accurate surface wind estimation such as wind-energy application, air-quality studies, and radioactive-pollutants dispersion studies. The surface drag generated by the subgrid-scale orography is represented by introducing a sink term in the momentum equation in their studies. The purpose of our study is to evaluate the simulated meteorological fields in the high-resolution WRF framework, that includes the parameterization of subgrid-scale orography developed by Mass and Ovens (2010), and enhance the forecast skill of low-level wind fields, which plays an important role in transport and dispersion of air pollutants including radioactive pollutants. The positive bias in 10-m wind speed is significantly alleviated by implementing the subgrid-scale orography parameterization, while other meteorological fields including 10-m wind direction are not changed. Increased variance of subgrid- scale orography enhances the sink of momentum and further reduces the bias in 10-m wind speed.

  18. Developing a Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Lambert, WInifred; Roeder, William

    2007-01-01

    This conference presentation describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations. The tool will include climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  19. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1995-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  20. An improved market penetration model for wind energy technology forecasting

    International Nuclear Information System (INIS)

    Lund, P.D.

    1995-01-01

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  1. Probabilistic modelling for forecasting the wind energy resource at the seasonal horizon

    Science.gov (United States)

    Alonzo, Bastien; Drobinski, Philippe; Plougonven, Riwal; Tankov, Peter

    2017-04-01

    We build and evaluate a probabilistic model designed for forecasting the distribution of the daily mean wind speed at the seasonal timescale. On such long-term timescales, numerical weather prediction models can bring valuable information on the large-scale circulation of the atmosphere which strongly influences surface wind speed. As an example, variations in the position of the storm track over the Atlantic directly impact surface winds in the North of France in autumn and winter. The model aims at predicting the daily mean wind speed distribution knowing the large scale situation of the atmosphere which is summarized by an index derived from the multi-polynomial regression between the 10 first Principal Components of the 500hPa geopotential height and the daily mean wind speed. The conditionnal probability density function of the wind speed knowing the index is estimated by a gaussian kernel density estimation over 20 years of daily reanalysis data. Evaluating the probabilistic model on a validation period of 15 years, we show that it is at least as well calibrated as the seasonal climatology which can be taken as a first guess prediction at such long-term horizon. We also show that the model is 20% sharper than the climatology in average, due to a less pronounced seasonal variability of the confidence interval width. We use the ECMWF seasonal forecast ensemble in order to predict the daily mean wind speed distribution at the seasonal timescale. The ensemble forecast, from which the index is derived, displays a growing uncertainty with time leading to an increase of the confidence interval width predicted by the probabilistic model. We show that the model remains sharper than the climatology at the monthly horizon, but tends to the climatological interval width after 30 days.

  2. Enhanced regional forecasting considering single wind farm distribution for upscaling

    International Nuclear Information System (INIS)

    Bremen, Lueder von; Saleck, Nadja; Heinemann, Detlev

    2007-01-01

    With increasing wind power penetration the need for more accurate wind power forecasts increases to raise the market value of wind power. State-of-the-art wind power forecasting tools are considered either statistical or physical. Fundamentally new techniques are rare, thus it is tried to establish a new approach. The spatial decomposition of wind power generation in Germany can be done with principle component analysis to extract the main pattern of variability. They have a physical meaning when linked with typical weather situation. The first four eigenvectors explain about 94 % of the observed variance. The time-evolving principle components are linked with the total wind power feed-in in Germany and are used for its estimation. A new wind power forecasting model has been implemented with this approach and shows very good results that are comparable with state-of-the-art commercial wind power forecast models. The day-ahead forecast error for a common intercomparison period Jan-Jul 2006 is 4.4 %. The suggested approach offers wide ranges for future developments (e.g. several NWP models), because it is computationally very cheap to run

  3. Adaptive calibration of (u,v)‐wind ensemble forecasts

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    Ensemble forecasts of (u,v)‐wind are of crucial importance for a number of decision‐making problems related to e.g. air traffic control, ship routeing and energy management. The skill of these ensemble forecasts as generated by NWP‐based models can be maximised by correcting for their lack of suf...

  4. Quantile Forecasting of Wind Power Using Variability Indices

    Directory of Open Access Journals (Sweden)

    Patrick McSharry

    2013-02-01

    Full Text Available Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. Using four years of wind power data from three wind farms in Denmark, we develop quantile regression models to generate short-term probabilistic forecasts from 15 min up to six hours ahead. More specifically, we investigate the potential of using various variability indices as explanatory variables in order to include the influence of changing weather regimes. These indices are extracted from the same wind power series and optimized specifically for each quantile. The forecasting performance of this approach is compared with that of appropriate benchmark models. Our results demonstrate that variability indices can increase the overall skill of the forecasts and that the level of improvement depends on the specific quantile.

  5. Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

    OpenAIRE

    Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang

    2016-01-01

    Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EE...

  6. Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei; Zhang, Lei

    2015-01-01

    Highlights: • Four hybrid algorithms are proposed for the wind speed decomposition. • Adaboost algorithm is adopted to provide a hybrid training framework. • MLP neural networks are built to do the forecasting computation. • Four important network training algorithms are included in the MLP networks. • All the proposed hybrid algorithms are suitable for the wind speed predictions. - Abstract: The technology of wind speed prediction is important to guarantee the safety of wind power utilization. In this paper, four different hybrid methods are proposed for the high-precision multi-step wind speed predictions based on the Adaboost (Adaptive Boosting) algorithm and the MLP (Multilayer Perceptron) neural networks. In the hybrid Adaboost–MLP forecasting architecture, four important algorithms are adopted for the training and modeling of the MLP neural networks, including GD-ALR-BP algorithm, GDM-ALR-BP algorithm, CG-BP-FR algorithm and BFGS algorithm. The aim of the study is to investigate the promoted forecasting percentages of the MLP neural networks by the Adaboost algorithm’ optimization under various training algorithms. The hybrid models in the performance comparison include Adaboost–GD-ALR-BP–MLP, Adaboost–GDM-ALR-BP–MLP, Adaboost–CG-BP-FR–MLP, Adaboost–BFGS–MLP, GD-ALR-BP–MLP, GDM-ALR-BP–MLP, CG-BP-FR–MLP and BFGS–MLP. Two experimental results show that: (1) the proposed hybrid Adaboost–MLP forecasting architecture is effective for the wind speed predictions; (2) the Adaboost algorithm has promoted the forecasting performance of the MLP neural networks considerably; (3) among the proposed Adaboost–MLP forecasting models, the Adaboost–CG-BP-FR–MLP model has the best performance; and (4) the improved percentages of the MLP neural networks by the Adaboost algorithm decrease step by step with the following sequence of training algorithms as: GD-ALR-BP, GDM-ALR-BP, CG-BP-FR and BFGS

  7. Forecast of wind energy production and ensuring required balancing power

    International Nuclear Information System (INIS)

    Merkulov, M.

    2010-01-01

    The wind energy is gaining larger part of the energy mix around the world as well as in Bulgaria. Having in mind the irregularity of the wind, we are in front of a challenge for management of the power grid in new unknown conditions. The world's experience has proven that there could be no effective management of the grid without forecasting tools, even with small scale of wind power penetration. Application of such tools promotes simple management of large wind energy production and reduction of the quantities of required balancing powers. The share of the expenses and efforts for forecasting of the wind energy is incomparably small in comparison with expenses for keeping additional powers in readiness. The recent computers potential allow simple and rapid processing of large quantities of data from different sources, which provides required conditions for modeling the world's climate and producing sophisticated forecast. (author)

  8. Latin America wind market assessment. Forecast 2013-2022

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-10-15

    Wind Power Activities by Country: Developers/Owners, Wind Plant Sizes, Wind Turbines Deployed, Commissioning Dates, Market Share, and Capacity Forecasts Latin American markets are a subject of intense interest from the global wind industry. Wind plant construction across Latin America is modest compared to the more established markets like the United States, Europe, and China, but it is an emerging market that is taking off at a rapid pace. The region has become the hottest alternative growth market for the wind energy industry at a time when growth rates in other markets are flat due to a variety of policy and macroeconomic challenges. Globalization is driving sustainable economic growth in most Latin American countries, resulting in greater energy demand. Wind is increasingly viewed as a valuable and essential answer to increasing electricity generation across most markets in Latin America. Strong wind resources, coupled with today's sophisticated wind turbines, are providing cost-effective generation that is competitive with fossil fuel generation. Most Latin American countries also rely heavily on hydroelectricity, which balances well with variable wind generation. Navigant Research forecasts that if most wind plants under construction with planned commissioning go online as scheduled, annual wind power installations in Latin America will grow from nearly 2.2 GW in 2013 to 4.3 GW by 2022. This Navigant Research report provides a comprehensive view of the wind energy market dynamics at play in Latin America. It offers a country-by-country analysis, outlining the key energy policies and development opportunities and barriers and identifying which companies own operational wind plants and which wind turbine vendors supplied those projects. Market forecasts for wind power installations, capacity, and market share in Latin America, segmented by country and company, extend through 2022. The report also offers an especially close analysis of Brazil and Mexico

  9. Use of wind power forecasting in operational decisions.

    Energy Technology Data Exchange (ETDEWEB)

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V. (Decision and Information Sciences); (INESC Porto)

    2011-11-29

    The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help

  10. An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power

    Directory of Open Access Journals (Sweden)

    Antonio Bracale

    2015-09-01

    Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.

  11. Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

    DEFF Research Database (Denmark)

    Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

    2013-01-01

    A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method...... is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at...

  12. Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method

    Energy Technology Data Exchange (ETDEWEB)

    Cadenas, E. [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Jaramillo, O.A.; Rivera, W. [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

    2010-05-15

    In this paper the analysis and forecasting of wind velocities in Chetumal, Quintana Roo, Mexico is presented. Measurements were made by the Instituto de Investigaciones Electricas (IIE) during two years, from 2004 to 2005. This location exemplifies the wind energy generation potential in the Caribbean coast of Mexico that could be employed in the hotel industry in the next decade. The wind speed and wind direction were measured at 10 m above ground level. Sensors with high accuracy and a low starting threshold were used. The wind velocity was recorded using a data acquisition system supplied by a 10 W photovoltaic panel. The wind speed values were measured with a frequency of 1 Hz and the average wind speed was recorded considering regular intervals of 10 min. First a statistical analysis of the time series was made in the first part of the paper through conventional and robust measures. Also the forecasting of the last day of measurements was made utilizing the single exponential smoothing method (SES). The results showed a very good accuracy of the data with this technique for an {alpha} value of 0.9. Finally the SES method was compared with the artificial neural network (ANN) method showing the former better results. (author)

  13. Torque- and Speed Control of a Pitch Regulated Wind Turbine

    Energy Technology Data Exchange (ETDEWEB)

    Rasila, Mika

    2003-07-01

    Variable speed operated wind turbines has the potential to reduce fatigue loads, compared to fixed speed wind turbines. With pitch controllable rotor blades limitation of the power at high wind speeds is obtained. The thesis describes different controlling aspects concerning wind turbines and how these together can be used to optimize the system's performance. Torque control is used in order to achieve reduction on the mechanical loads on the drive-train for low wind speeds and limitation of power output for high wind speeds. In the high wind speed interval torque control is effective in order to limit the output power if a sufficiently fast pitch actuator is used. In the middle wind speed interval filter utilization can be used to give a reference signal to the controller in order to reduce speed and torque variations.

  14. Results of verification and investigation of wind velocity field forecast. Verification of wind velocity field forecast model

    International Nuclear Information System (INIS)

    Ogawa, Takeshi; Kayano, Mitsunaga; Kikuchi, Hideo; Abe, Takeo; Saga, Kyoji

    1995-01-01

    In Environmental Radioactivity Research Institute, the verification and investigation of the wind velocity field forecast model 'EXPRESS-1' have been carried out since 1991. In fiscal year 1994, as the general analysis, the validity of weather observation data, the local features of wind field, and the validity of the positions of monitoring stations were investigated. The EXPRESS which adopted 500 m mesh so far was improved to 250 m mesh, and the heightening of forecast accuracy was examined, and the comparison with another wind velocity field forecast model 'SPEEDI' was carried out. As the results, there are the places where the correlation with other points of measurement is high and low, and it was found that for the forecast of wind velocity field, by excluding the data of the points with low correlation or installing simplified observation stations to take their data in, the forecast accuracy is improved. The outline of the investigation, the general analysis of weather observation data and the improvements of wind velocity field forecast model and forecast accuracy are reported. (K.I.)

  15. Sharing wind power forecasts in electricity markets: A numerical analysis

    International Nuclear Information System (INIS)

    Exizidis, Lazaros; Kazempour, S. Jalal; Pinson, Pierre; Greve, Zacharie de; Vallée, François

    2016-01-01

    Highlights: • Information sharing among different agents can be beneficial for electricity markets. • System cost decreases by sharing wind power forecasts between different agents. • Market power of wind producer may increase by sharing forecasts with market operator. • Extensive out-of-sample analysis is employed to draw reliable conclusions. - Abstract: In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts.

  16. Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

    Directory of Open Access Journals (Sweden)

    Constantin Junk

    2015-04-01

    Full Text Available Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn, which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS minimization and a principal component analysis (PCA of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF, including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10‑m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

  17. Control of Variable Speed Variable Pitch Wind Turbine at Above and Below Rated Wind Speed

    Directory of Open Access Journals (Sweden)

    Saravanakumar Rajendran

    2014-01-01

    Full Text Available The paper presents a nonlinear approach to wind turbine (WT using two-mass model. The main aim of the controller in the WT is to maximize the energy output at varying wind speed. In this work, a combination of linear and nonlinear controllers is adapted to variable speed variable pitch wind turbines (VSVPWT system. The major operating regions of the WT are below (region 2 and above rated (region 3 wind speed. In these regions, generator torque control (region 2 and pitch control (region 3 are used. The controllers in WT are tested for below and above rated wind speed for step and vertical wind speed profile. The performances of the controllers are analyzed with nonlinear FAST (Fatigue, Aerodynamics, Structures, and Turbulence WT dynamic simulation. In this paper, two nonlinear controllers, that is, sliding mode control (SMC and integral sliding mode control (ISMC, have been applied for region 2, whereas for pitch control in region 3 conventional PI control is used. In ISMC, the sliding manifold makes use of an integral action to show effective qualities of control in terms of the control level reduction and sliding mode switching control minimization.

  18. Development and testing of improved statistical wind power forecasting methods.

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

  19. Variability of Wind Speeds and Power over Europe

    Science.gov (United States)

    Tambke, J.; von Bremen, L.; de Decker, J.; Schmidt, M.; Steinfeld, G.; Wolff, J.-O.

    2010-09-01

    This study comprises two parts: First, we describe the vertical wind speed and turbulence profiles that result from our improved PBL scheme and compare it to observations and 1-dimensional approaches (Monin-Obukhov etc.). Second, we analyse the spatio-temporal correlations in our meso-scale simulations for the years 2004 to 2007 over entire Europe, with special focus on the Irish, North and Baltic Sea. 1.) Vertical Wind Speed Profiles The vertical wind profile above the sea has to be modelled with high accuracy for tip heights up to 160m in order to achieve precise wind resource assessments, to calculate loads and wakes of wind turbines as well as for reliable short-term wind power forecasts. We present an assessment of different models for wind profiles in unstable, neutral and stable thermal stratification. The meso-scale models comprise MM5, WRF and COSMO-EU (LME). Both COSMO-EU from the German Weather Service DWD and WRF use a turbulence closure of 2.5th order - and lead to similar results. Especially the limiting effect of low boundary layer heights on the wind shear in very stable stratification is well captured. In our new WRF-formulation for the mixing length in the Mellor-Yamada-Janjic (MYJ) parameterisation of the Planetary Boundary Layer (PBL-scheme), the master length scale itself depends on the Monin-Obukhov-Length as a parameter for the heat flux effects on the turbulent mixing. This new PBL-scheme shows a better performance for all weather conditions than the original MYJ-scheme. Apart from the low-boundary-layer-effect in very stable situations (which are seldom), standard Monin-Obukhov formulations in combination with the Charnock relation for the sea surface roughness show good agreement with the FINO1-data (German Bight). Interesting results were achieved with two more detailed micro-scale approaches: - the parameterization proposed by Pena, Gryning and Hasager [BLM 2008] that depends on the boundary layer height - our ICWP-model, were the flux

  20. Wind speed prediction using statistical regression and neural network

    Indian Academy of Sciences (India)

    Four different statistical techniques,viz.,curve fitting,Auto Regressive Integrated Moving Average Model (ARIMA),extrapolation with periodic function and Artificial Neural Networks (ANN)are employed to predict wind speed.These methods require wind speeds of previous hours as input.It has been found that wind speed can ...

  1. statistical analysis of wind speed for electrical power generation

    African Journals Online (AJOL)

    HOD

    Keywords: Wind speed - probability - density function – wind energy conversion system- statistical analyses. 1. INTRODUCTION. In order ..... "Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in. Garoua, Cameroon," Revue des Energies. Renouvelables, vol. 18, no. 1, pp.

  2. Observer Backstepping Control for Variable Speed Wind Turbine

    DEFF Research Database (Denmark)

    Galeazzi, Roberto; Gryning, Mikkel Peter Sidoroff; Blanke, Mogens

    2013-01-01

    This paper presents an observer backstepping controller as feasible solution to variable speed control of wind turbines to maximize wind power capture when operating between cut-in and rated wind speeds. The wind turbine is modeled as a two-mass drive-train system controlled by the generator torq...

  3. Observer Backstepping Control for Variable Speed Wind Turbine

    DEFF Research Database (Denmark)

    Galeazzi, Roberto; Gryning, Mikkel Peter Sidoroff; Blanke, Mogens

    2013-01-01

    This paper presents an observer backstepping controller as feasible solution to variable speed control of wind turbines to maximize wind power capture when operating between cut-in and rated wind speeds. The wind turbine is modeled as a two-mass drive-train system controlled by the generator torque...

  4. A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2012-01-01

    Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes...... fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind...... power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov...

  5. Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm

    OpenAIRE

    BeomJun Park; Jin Hur

    2017-01-01

    Short-term wind power forecasting is a technique which tells system operators how much wind power can be expected at a specific time. Due to the increasing penetration of wind generating resources into the power grids, short-term wind power forecasting is becoming an important issue for grid integration analysis. The high reliability of wind power forecasting can contribute to the successful integration of wind generating resources into the power grids. To guarantee the reliability of forecas...

  6. Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

    International Nuclear Information System (INIS)

    Woolsey, Lauren N.; Cranmer, Steven R.

    2014-01-01

    Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvén waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

  7. Comparison of SAR Wind Speed Retrieval Algorithms for Evaluating Offshore Wind Energy Resources

    DEFF Research Database (Denmark)

    Kozai, K.; Ohsawa, T.; Takeyama, Y.

    2010-01-01

    Envisat/ASAR-derived offshore wind speeds and energy densities based on 4 different SAR wind speed retrieval algorithms (CMOD4, CMOD-IFR2, CMOD5, CMOD5.N) are compared with observed wind speeds and energy densities for evaluating offshore wind energy resources. CMOD4 ignores effects of atmospheric...

  8. Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle

    Directory of Open Access Journals (Sweden)

    Tobias Heppelmann

    2017-06-01

    Full Text Available For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP. With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD, against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

  9. An Analysis of Variable-Speed Wind Turbine Power-Control Methods with Fluctuating Wind Speed

    Directory of Open Access Journals (Sweden)

    Seung-Il Moon

    2013-07-01

    Full Text Available Variable-speed wind turbines (VSWTs typically use a maximum power-point tracking (MPPT method to optimize wind-energy acquisition. MPPT can be implemented by regulating the rotor speed or by adjusting the active power. The former, termed speed-control mode (SCM, employs a speed controller to regulate the rotor, while the latter, termed power-control mode (PCM, uses an active power controller to optimize the power. They are fundamentally equivalent; however, since they use a different controller at the outer control loop of the machine-side converter (MSC controller, the time dependence of the control system differs depending on whether SCM or PCM is used. We have compared and analyzed the power quality and the power coefficient when these two different control modes were used in fluctuating wind speeds through computer simulations. The contrast between the two methods was larger when the wind-speed fluctuations were greater. Furthermore, we found that SCM was preferable to PCM in terms of the power coefficient, but PCM was superior in terms of power quality and system stability.

  10. Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

    Science.gov (United States)

    Fernández-González, S.; Martín, M. L.; Merino, A.; Sánchez, J. L.; Valero, F.

    2017-04-01

    During recent decades, the use of probabilistic forecasting methods has increased markedly. However, these predictions still need improvement in uncertainty quantification and predictability analysis. For this reason, the main aim of this paper is to develop tools for quantifying uncertainty and predictability of wind speed over the Iberian Peninsula. To achieve this goal, several spread indexes extracted from an ensemble prediction system are defined in this paper. Subsequently, these indexes were evaluated with the aim of selecting the most appropriate for the characterization of uncertainty associated to the forecasting. Selection is based on comparison of the average magnitude of ensemble spread (ES) and mean absolute percentage error (MAPE). MAPE is estimated by comparing the ensemble mean with wind speed values from different databases. Later, correlation between MAPE and ES was evaluated. Furthermore, probability distribution functions (PDFs) of spread indexes are analyzed to select the index with greater similarity to MAPE PDFs. Then, the spread index selected as optimal is used to carry out a spatiotemporal analysis of model uncertainty in wind forecasting. The results indicate that mountainous regions and the Mediterranean coast are characterized by strong uncertainty, and the spread increases more rapidly in areas affected by strong winds. Finally, a predictability index is proposed for obtaining a tool capable of providing information on whether the predictability is higher or lower than average. The applications developed may be useful in the forecasting of wind potential several days in advance, with substantial importance for estimating wind energy production.

  11. Trading wind generation from short-term probabilistic forecasts of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Chevallier, Christophe; Kariniotakis, Georges

    2007-01-01

    Due to the fluctuating nature of the wind resource, a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs. Accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue...... participation. Such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation. This paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation....... Despite the fact that increasing accuracy in spot forecasts may reduce penalties, this paper shows that, if such forecasts are accompanied with information on their uncertainty, i.e., in the form of predictive distributions, then this can be the basis for defining advanced strategies for market...

  12. How do fungi measure wind speed?

    Science.gov (United States)

    Roper, Marcus; Tomasek, Michael; Lin, Yuxi; Dressaire, Emilie

    2017-11-01

    For successful dispersal, a fungus must push its spores through the boundary layer of nearly still air that clings to it. Some spores pass through this boundary layer by being forcibly ejected. But many spores and fungi have no mechanism for active ejection, and must be carried away passively by the wind. To facilitate dispersal, the spores are borne on the top of aerial structures. Regulating the height of these aerial structures is an engineering challenge; too long and the structures will collapse under their own weight; too short, and they may not reach high enough to cross the boundary layer. A fungus therefore benefits by knowing the wind speed (and therefore the boundary layer thickness). How does it make this measurement? I will show that the model filamentous fungus Neurospora crassa uses water evaporation rate to accurately measure wind speed. In addition to showing that fungi control and optimize even passive mechanisms for dispersal, our findings highlight the importance of physical conditions in controlling fungal growth and behavior.

  13. Wind speed change regionalization in China (1961–2012

    Directory of Open Access Journals (Sweden)

    Pei-Jun Shi

    2015-06-01

    Full Text Available This research quantitatively recognized the wind speed change using wind speed trend and trend of wind speed variability from 1961 to 2012 and regionalized the wind speed change on a county-level basis. The mean wind speed observation data and linear fitting method were used. The findings suggested that level-I regionalization includes six zones according to wind speed trend value in different regions, viz. Northeast China–North China substantial declining zone, East–Central China declining zone, Southeast China slightly declining zone, Southwest China very slightly declining zone, Northwest China declining zone, and Qinghai–Tibetan Plateau slightly declining zone. Level-II regionalization divides China into twelve regions based on trend of wind speed variability and the level-I regionalization results.

  14. Estimation of the uncertainty in wind power forecasting

    International Nuclear Information System (INIS)

    Pinson, P.

    2006-03-01

    WIND POWER experiences a tremendous development of its installed capacities in Europe. Though, the intermittence of wind generation causes difficulties in the management of power systems. Also, in the context of the deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3-day ahead eases the integration of wind generation. Wind power forecasts are traditionally provided in the form of point predictions, which correspond to the most-likely power production for a given horizon. That sole information is not sufficient for developing optimal management or trading strategies. Therefore, we investigate on possible ways for estimating the uncertainty of wind power forecasts. The characteristics of the prediction uncertainty are described by a thorough study of the performance of some of the state-of-the-art approaches, and by underlining the influence of some variables e.g. level of predicted power on distributions of prediction errors. Then, a generic method for the estimation of prediction intervals is introduced. This statistical method is non-parametric and utilizes fuzzy logic concepts for integrating expertise on the prediction uncertainty characteristics. By estimating several prediction intervals at once, one obtains predictive distributions of wind power output. The proposed method is evaluated in terms of its reliability, sharpness and resolution. In parallel, we explore the potential use of ensemble predictions for skill forecasting. Wind power ensemble forecasts are obtained either by converting meteorological ensembles (from ECMWF and NCEP) to power or by applying a poor man's temporal approach. A proposal for the definition of prediction risk indices is given, reflecting the disagreement between ensemble members over a set of successive look-ahead times. Such prediction risk indices may comprise a more comprehensive signal on the expected level

  15. Forecasting for Wind Power Plants in Quebec, Canada

    Science.gov (United States)

    Dyck, Sarah

    2010-05-01

    The SPEO project (Système de Prévisions ÉOlienne) is a collaborative research effort between Environment Canada (EC) and Hydro-Québec (HQ) and has been in operation since May of 2007. It provides a 48 hour high resolution (2.5 km) wind forecast, four times daily, in order to assist in the successful management of wind power from plants in the Gaspé Region of Québec, Canada. Early in 2010, the number of forecasts increased to four times a day. The Canadian Global Environmental Multiscale-Limited Area Model (GEM-LAM), at the heart of this system, is driven by the operational regional forecasts at 15 km resolution from the Canadian Meteorological Centre. This system has been evaluated using observations from EC meteorological stations and special masts installed at wind power plants and the results will be discussed. Specifically, an effort was made to examine the predictability of rare events critical in the operations of wind power plants such as strong winds and high atmospheric turbulence, which can force wind turbines to shut down. Future research for the improvement of this forecasting system will also be presented.

  16. Introducing distributed learning approaches in wind power forecasting

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2016-01-01

    Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks...... to substantial deployment of generating capacities and increased temporal resolution, it may now be possible to build large models accounting for all space-time dependencies. This will eventually allow to significantly improve the quality of short-term renewable power forecasts. However, in practice, it is often...... to large datasets in Australia (22 wind farms) and France (85 wind farms) are used to illustrate the interest and performance of our proposal....

  17. Prediction models for wind speed at turbine locations in a wind farm

    DEFF Research Database (Denmark)

    Knudsen, Torben; Bak, Thomas; Soltani, Mohsen

    2011-01-01

    turbulence models. The esti- mator includes a nonlinear time varying wind speed model, which compared with literature results in an adaptive filter. Given the estimated effective wind speed, it is possible to establish wind speed prediction models by system identification. As the prediction models are based...... manifested through the wind field is hence required. This paper develops models for this relationship. The result is based on two new contributions: the first is related to the estimation of effective wind speeds, which serves as a basis for the second contribution to wind speed prediction models. Based...... on standard turbine measurements such as rotor speed and power produced, an effective wind speed, which represents the wind field averaged over the rotor disc, is derived. The effective wind speed estimator is based on a continuous–discrete extended Kalman filter that takes advantage of nonlinear time varying...

  18. Battlescale Forecast Model Sensitivity Study

    National Research Council Canada - National Science Library

    Sauter, Barbara

    2003-01-01

    .... Changes to the surface observations used in the Battlescale Forecast Model initialization led to no significant changes in the resulting forecast values of temperature, relative humidity, wind speed, or wind direction...

  19. Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

    2017-01-01

    Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants...... by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare as well...... as the profits of participating power producers. We investigate, therefore, a joint day-ahead energy and reserve auction, where producers offer their conventional power strategically based on a complementarity approach and their wind power at generation cost based on a forecast. In parallel, an iterative game...

  20. The impact of scatterometer wind data on global weather forecasting

    Science.gov (United States)

    Atlas, D.; Baker, W. E.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.

    1984-01-01

    The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets.

  1. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were

  2. High resolution forecasting for wind energy applications using Bayesian model averaging

    Energy Technology Data Exchange (ETDEWEB)

    Courtney, Jennifer F.; Lynch, Peter; Sweeney, Conor [Meteorology and Climate Centre, UCD, Dublin (Ireland)], e-mail: jennifer.courtney@ucdconnect.ie

    2013-02-15

    Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.

  3. High resolution forecasting for wind energy applications using Bayesian model averaging

    Directory of Open Access Journals (Sweden)

    Jennifer F. Courtney

    2013-02-01

    Full Text Available Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF ensemble prediction system (EPS are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA. The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.

  4. Skill forecasting from different wind power ensemble prediction methods

    International Nuclear Information System (INIS)

    Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

    2007-01-01

    This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

  5. Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data

    Science.gov (United States)

    Wang, Han; Yan, Jie; Liu, Yongqian; Han, Shuang; Li, Li; Zhao, Jing

    2017-11-01

    Increasing the accuracy of wind speed prediction lays solid foundation to the reliability of wind power forecasting. Most traditional correction methods for wind speed prediction establish the mapping relationship between wind speed of the numerical weather prediction (NWP) and the historical measurement data (HMD) at the corresponding time slot, which is free of time-dependent impacts of wind speed time series. In this paper, a multi-step-ahead wind speed prediction correction method is proposed with consideration of the passing effects from wind speed at the previous time slot. To this end, the proposed method employs both NWP and HMD as model inputs and the training labels. First, the probabilistic analysis of the NWP deviation for different wind speed bins is calculated to illustrate the inadequacy of the traditional time-independent mapping strategy. Then, support vector machine (SVM) is utilized as example to implement the proposed mapping strategy and to establish the correction model for all the wind speed bins. One Chinese wind farm in northern part of China is taken as example to validate the proposed method. Three benchmark methods of wind speed prediction are used to compare the performance. The results show that the proposed model has the best performance under different time horizons.

  6. Estimation of Rotor Effective Wind Speed: A Comparison

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Knudsen, Torben; Svenstrup, Mikael

    2013-01-01

    Modern wind turbine controllers use wind speed information to improve power production and reduce loads on the turbine components. The turbine top wind speed measurement is unfortunately imprecise and not a good representative of the rotor effective wind speed. Consequently, many different model......-based algorithms have been proposed that are able to estimate the wind speed using common turbine measurements. In this paper, we present a concise yet comprehensive analysis and comparison of these techniques, reviewing their advantages and drawbacks. We implement these techniques and compare the results on both...

  7. NWP Forecast Errors of Boundary Layer Flow in Complex Terrain Observed During the Second Wind Forecast Improvement Project (WFIP2) Field Campaign.

    Science.gov (United States)

    Wilczak, James M.

    2017-04-01

    The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy and NOAA-led program whose goal is to improve the accuracy of NWP forecasts of wind speed in complex terrain for wind energy applications. WFIP2 includes a field campaign held in the vicinity of the Columbia River Basin in the Pacific Northwest of the U.S., which began in October 2015, and will continue through March, 2017. As part of WFIP2 a large suite of in-situ and remote sensing instrumentation has been deployed, including a network of three 449 MHz radar wind profilers (RWP's) with RASS, eight 915 MHz RWP's with RASS, 18 sodars, 4 profiling microwave radiometers, 5 scanning lidars, 5 profiling lidars, a network of 10 microbarographs, and many surface meteorological stations. Key NWP forecast models utilized for WFIP2 are the 13 km resolution Rapid Refresh (RAP), 3km High Resolution Rapid Refresh (HRRR), 0.75km HRRR-Nest, and the 12 km North American Mesoscale (NAM) forecast system. Preliminary results from WFIP2 will be presented, including seasonal variations of model forecast errors of wind speed, direction, temperature and humidity profiles and boundary layer depths; meteorological phenomena producing large forecast errors; and the relative skill of the various NWP forecasting systems. Diurnal time height cross-sections of the model's mean bias and RMSE are evaluated for each of the models, providing a holistic view of model accuracy at simulating boundary layer structure. Model errors are analyzed as a function of season (3 month averages) and location, and show the impact of increasing model resolution on forecast skill. Seasonal averages of model biases and RMSE provide more robust results than do shorter case study episodes, and can be used to verify that model errors found in shorter case study episodes are in fact representative. The results are used to identify specific model weaknesses and the corresponding parameterization schemes that are in greatest need of

  8. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

    International Nuclear Information System (INIS)

    Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

    2017-01-01

    Highlights: • An ensemble model is developed to produce both deterministic and probabilistic wind forecasts. • A deep feature selection framework is developed to optimally determine the inputs to the forecasting methodology. • The developed ensemble methodology has improved the forecasting accuracy by up to 30%. - Abstract: With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

  9. Calibration of a spinner anemometer for wind speed measurements

    DEFF Research Database (Denmark)

    Demurtas, Giorgio; Friis Pedersen, Troels; Zahle, Frederik

    2016-01-01

    constant related to wind speed measurements. The first and preferred method is based on the definition of the calibration constant and uses wind speed measurements during the stopped condition of the wind turbine. Two alternative methods that did not require the turbine to be stopped were investigated: one...

  10. Stochastic Simulation of Hourly Average Wind Speed in Umudike ...

    African Journals Online (AJOL)

    Ten years of hourly average wind speed data were used to build a seasonal autoregressive integrated moving average (SARIMA) model. The model was used to simulate hourly average wind speed and recommend possible uses at Umudike, South eastern Nigeria. Results showed that the simulated wind behaviour was ...

  11. Wind speed prediction using statistical regression and neural network

    Indian Academy of Sciences (India)

    Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess- ment,satellite launching and aviation,etc.There are a few techniques available for wind speed prediction,which require a minimum number of input parameters.Four different statistical techniques,viz.,curve fitting,Auto Regressive ...

  12. The economics of a variable speed wind-diesel

    International Nuclear Information System (INIS)

    Moll, W.

    1992-01-01

    A remote community power supply system generating over 1,000 kWH/d will have at least one diesel generator running all the time. If one or more wind turbine generators are added to such a system, the diesel generator will produce less power when wind speeds are adequate, but its fuel efficiency will gradually decrease as load decreases. In the variable speed wind/diesel concept, the diesel rpm is reduced with decreasing load and a high fuel efficiency is maintained over virtually the full power range. The outputs of the diesel and wind turbine generators are fed into an inverter which synthesizes a desired voltage wave-shape with controlled magnitude and frequency. The variable speed wind/diesel concept may make vertical axis wind turbines suitable for remote community power supply because the inverter effectively isolates the power ripple of the wind turbine. A possible wind/diesel system configuration using the variable speed concept is illustrated. The economics of a 50-kW variable speed diesel and a 80-kW variable speed wind turbine generator was analyzed. Going from a constant speed diesel generator to a variable speed generator operating at 55% capacity factor, a 6% fuel saving was achieved. Adding one 80-kW wind turbine increased fuel savings to 32% at 5 m/s wind speed, but the unit energy cost rose 8.5%. At 7 m/s wind speed, fuel savings were 59% and energy savings were 7.8%. Economics are better for a peaking variable speed 50-kW wind/diesel system added to an existing diesel system to extend the installed capacity. At 7 m/s wind speed the fuel savings translate into ca $40,000 over 10 y and purchase of a $150,000 diesel generator is postponed. 7 figs., 1 tab

  13. Statistical learning for wind power: A modeling and stability study towards forecasting

    Science.gov (United States)

    Fischer, Aurélie; Montuelle, Lucie; Mougeot, Mathilde; Picard, Dominique

    2017-12-01

    We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{\\"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CART-Bagging algorithm gives very stable and promising results. Besides, as a step towards forecast, we quantify the impact of using deteriorated wind measures on the performances. We show also on this application that the default methodology to select a subset of predictors provided in the standard random forest package can be refined, especially when there exists among the predictors one variable which has a major impact.

  14. Climatology and trend of wind power resources in China and its surrounding regions: a revisit using Climate Forecast System Reanalysis data

    Science.gov (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

    2015-01-01

    The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis...

  15. Real Time Wave Forecasting Using Wind Time History and Genetic Programming

    Directory of Open Access Journals (Sweden)

    A.R. Kambekar

    2014-12-01

    Full Text Available The significant wave height and average wave period form an essential input for operational activities in ocean and coastal areas. Such information is important in issuing appropriate warnings to people planning any construction or instillation works in the oceanic environment. Many countries over the world routinely collect wave and wind data through a network of wave rider buoys. The data collecting agencies transmit the resulting information online to their registered users through an internet or a web-based system. Operational wave forecasts in addition to the measured data are also made and supplied online to the users. This paper discusses operational wave forecasting in real time mode at locations where wind rather than wave data are continuously recorded. It is based on the time series modeling and incorporates an artificial intelligence technique of genetic programming. The significant wave height and average wave period values are forecasted over a period of 96 hr in future from the observations of wind speed and directions extending to a similar time scale in the past. Wind measurements made by floating buoys at eight different locations around India over a period varying from 1.5 yr to 9.0 yr were considered. The platform of Matlab and C++ was used to develop a graphical user interface that will extend an internet based user-friendly access of the forecasts to any registered user of the data dissemination authority.

  16. Effectiveness of Changing Wind Turbine Cut-in Speed to Reduce Bat Fatalities at Wind Facilities

    Energy Technology Data Exchange (ETDEWEB)

    Huso, Manuela M. P. [Oregon State Univ., Corvallis, OR (United States); Hayes, John P. [Univ. of Florida, Gainesville, FL (United States)

    2009-04-01

    This report details an experiment on the effectiveness of changing wind turbine cut-in speed on reducing bat fatality from wind turbines at the Casselman Wind Project in Somerset County, Pennsylvania.

  17. Time Series Model of Wind Speed for Multi Wind Turbines based on Mixed Copula

    Directory of Open Access Journals (Sweden)

    Nie Dan

    2016-01-01

    Full Text Available Because wind power is intermittent, random and so on, large scale grid will directly affect the safe and stable operation of power grid. In order to make a quantitative study on the characteristics of the wind speed of wind turbine, the wind speed time series model of the multi wind turbine generator is constructed by using the mixed Copula-ARMA function in this paper, and a numerical example is also given. The research results show that the model can effectively predict the wind speed, ensure the efficient operation of the wind turbine, and provide theoretical basis for the stability of wind power grid connected operation.

  18. A nonlinear dynamics approach for incorporating wind-speed patterns into wind-power project evaluation.

    Science.gov (United States)

    Huffaker, Ray; Bittelli, Marco

    2015-01-01

    Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind-the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns.

  19. Stochastic generation of hourly wind speed time series

    International Nuclear Information System (INIS)

    Shamshad, A.; Wan Mohd Ali Wan Hussin; Bawadi, M.A.; Mohd Sanusi, S.A.

    2006-01-01

    In the present study hourly wind speed data of Kuala Terengganu in Peninsular Malaysia are simulated by using transition matrix approach of Markovian process. The wind speed time series is divided into various states based on certain criteria. The next wind speed states are selected based on the previous states. The cumulative probability transition matrix has been formed in which each row ends with 1. Using the uniform random numbers between 0 and 1, a series of future states is generated. These states have been converted to the corresponding wind speed values using another uniform random number generator. The accuracy of the model has been determined by comparing the statistical characteristics such as average, standard deviation, root mean square error, probability density function and autocorrelation function of the generated data to those of the original data. The generated wind speed time series data is capable to preserve the wind speed characteristics of the observed data

  20. statistical analysis of wind speed for electrical power generation

    African Journals Online (AJOL)

    HOD

    1, 4, 5 DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING, UNIVERSITY OF ILORIN, KWARA STATE, NIGERIA. 2DEPARTMENT OF ... Keywords: Wind speed - probability - density function – wind energy conversion system- statistical analyses. 1. ..... weather data for energy assessments of hybrid.

  1. On the market impact of wind energy forecasts

    International Nuclear Information System (INIS)

    Jonsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

    2010-01-01

    This paper presents an analysis of how day-ahead electricity spot prices are affected by day-ahead wind power forecasts. Demonstration of this relationship is given as a test case for the Western Danish price area of the Nord Pool's Elspot market. Impact on the average price behaviour is investigated as well as that on the distributional properties of the price. By using a non-parametric regression model to assess the effects of wind power forecasts on the average behaviour, the non-linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial. Furthermore, by evaluating the distributional properties of the spot prices under different scenarios, the impact of the wind power forecasts on the price distribution is proved to be considerable. The conditional price distribution is moreover shown to be non-Gaussian. This implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have Gaussian residuals. Hence the widespread assumption of Gaussian residuals from electricity spot price models is shown to be inadequate for these model types. The revealed effects are likely to be observable and qualitatively similar in other day-ahead electricity markets significantly penetrated by wind power. (author)

  2. Evaluation of Nonparametric Probabilistic Forecasts of Wind Power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008

    Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...

  3. Forecast of icing events at a wind farm in Sweden

    DEFF Research Database (Denmark)

    Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik

    2014-01-01

    This paper introduces a method for identifying icing events using a physical icing model, driven by atmospheric data from the Weather Research and Forecasting (WRF) model, and applies it to a wind park in Sweden. Observed wind park icing events were identified by deviation from an idealized power...... curve and observed temperature. The events were modeled using a physical icing model with equations for both accretion and ablation mechanisms (iceBlade). The accretion model is based on the Makkonen model but was modified to make it applicable to the blades of a wind turbine rather than a static...

  4. The influence of humidity fluxes on offshore wind speed profiles

    DEFF Research Database (Denmark)

    Barthelmie, Rebecca Jane; Sempreviva, Anna Maria; Pryor, Sara

    2010-01-01

    extrapolation from lower measurements. With humid conditions and low mechanical turbulence offshore, deviations from the traditional logarithmic wind speed profile become significant and stability corrections are required. This research focuses on quantifying the effect of humidity fluxes on stability corrected...... wind speed profiles. The effect on wind speed profiles is found to be important in stable conditions where including humidity fluxes forces conditions towards neutral. Our results show that excluding humidity fluxes leads to average predicted wind speeds at 150 m from 10 m which are up to 4% higher...... than if humidity fluxes are included, and the results are not very sensitive to the method selected to estimate humidity fluxes....

  5. SSMI wind speed measurements over the Southern Hemisphere oceans

    Science.gov (United States)

    Halpern, David

    1993-01-01

    The Special Sensor Microwave Imager (SSMI) carried by the USAF Defense Meteorological Satellite Program spacecraft correlates the intensity of microwave radiation emitted at the ocean surface with the 10-m height wind speed. The 4-year mean global features of the SSMI data were similar to the climatological-mean annual wind speed estimated from ship reports. Time series of area-weighted 60 deg S - zero deg monthly mean wind speeds indicated that the South Indian Ocean had the largest wind speeds throughout the year.

  6. An Appropriate Wind Model for Wind Integrated Power Systems Reliability Evaluation Considering Wind Speed Correlations

    Directory of Open Access Journals (Sweden)

    Rajesh Karki

    2013-02-01

    Full Text Available Adverse environmental impacts of carbon emissions are causing increasing concerns to the general public throughout the world. Electric energy generation from conventional energy sources is considered to be a major contributor to these harmful emissions. High emphasis is therefore being given to green alternatives of energy, such as wind and solar. Wind energy is being perceived as a promising alternative. This source of energy technology and its applications have undergone significant research and development over the past decade. As a result, many modern power systems include a significant portion of power generation from wind energy sources. The impact of wind generation on the overall system performance increases substantially as wind penetration in power systems continues to increase to relatively high levels. It becomes increasingly important to accurately model the wind behavior, the interaction with other wind sources and conventional sources, and incorporate the characteristics of the energy demand in order to carry out a realistic evaluation of system reliability. Power systems with high wind penetrations are often connected to multiple wind farms at different geographic locations. Wind speed correlations between the different wind farms largely affect the total wind power generation characteristics of such systems, and therefore should be an important parameter in the wind modeling process. This paper evaluates the effect of the correlation between multiple wind farms on the adequacy indices of wind-integrated systems. The paper also proposes a simple and appropriate probabilistic analytical model that incorporates wind correlations, and can be used for adequacy evaluation of multiple wind-integrated systems.

  7. Forecasting Production Losses at a Swedish Wind Farm

    DEFF Research Database (Denmark)

    Production loss due to icing has been identified as a problem both when siting turbines in cold climates, and when making forecasts of energy production for wind park management and energy markets. The Makkonen icing model (Makkonen, 2000), driven by output from the WRF mesoscale model, has been...... shown to predict periods of icing at a wind farm in northern Sweden (Davis et al, 2012) with improved skill compared to persistence and threshold models. Based on these results, we have developed a statistical model to estimate the loss of production at the wind park due to these icing periods. We...... compared this statistical model with a simpler method that does not rely on a physical icing model. In that method meteorological icing is identified as periods when WRF forecasts clouds and the temperature is below freezing. During these periods it is assumed that there is no production from the turbines...

  8. Advanced mesoscale forecasts of icing events for Gaspe wind farms

    International Nuclear Information System (INIS)

    Gayraud, A.; Benoit, R.; Camion, A.

    2009-01-01

    Atmospheric icing includes every event which causes ice accumulations of various shapes on different structures. In terms of its effects on wind farms, atmospheric icing can decrease the aerodynamic performance, cause structure overloading, and add vibrations leading to failure and breaking. This presentation discussed advanced mesoscale forecasts of icing events for Gaspe wind farms. The context of the study was discussed with particular reference to atmospheric icing; effects on wind farms; and forecast objectives. The presentation also described the models and results of the study. These included MC2, a compressible community model, as well as a Milbrandt and Yau condensation scheme. It was shown that the study has provided good estimates of the duration of events as well as reliable precipitation categories. tabs., figs.

  9. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme.

    Science.gov (United States)

    Owens, Mathew J; Riley, Pete

    2017-11-01

    Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

  10. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme

    Science.gov (United States)

    Owens, Mathew J.; Riley, Pete

    2017-11-01

    Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

  11. A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

    International Nuclear Information System (INIS)

    Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.

    2013-01-01

    Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability

  12. A multiple-fan active control wind tunnel for outdoor wind speed and direction simulation

    Science.gov (United States)

    Wang, Jia-Ying; Meng, Qing-Hao; Luo, Bing; Zeng, Ming

    2018-03-01

    This article presents a new type of active controlled multiple-fan wind tunnel. The wind tunnel consists of swivel plates and arrays of direct current fans, and the rotation speed of each fan and the shaft angle of each swivel plate can be controlled independently for simulating different kinds of outdoor wind fields. To measure the similarity between the simulated wind field and the outdoor wind field, wind speed and direction time series of two kinds of wind fields are recorded by nine two-dimensional ultrasonic anemometers, and then statistical properties of the wind signals in different time scales are analyzed based on the empirical mode decomposition. In addition, the complexity of wind speed and direction time series is also investigated using multiscale entropy and multivariate multiscale entropy. Results suggest that the simulated wind field in the multiple-fan wind tunnel has a high degree of similarity with the outdoor wind field.

  13. How El Niño can be used to improve wind speed seasonal skill?

    Science.gov (United States)

    Gonzalez-Reviriego, Nube; Marcos, Raül; Doblas-Reyes, Francisco J.; Torralba, Verónica; Cortesi, Nicola; Lee, Doo Young; Soret, Albert

    2017-04-01

    The potential benefit of seasonal wind speed forecasts for the energy sector has been recently discussed (Torralba et al. 2016, Buontempo et al. 2016). Nevertheless, the lack of skill over several inland areas and especially at high lead times, can limit the application of these seasonal probabilistic forecasts. By using a simple methodology approach, this study aims to illustrate how the scientific user-driven research, conducted in a context of climate services, should play a role in the improvement of the wind speed seasonal forecast skill. In this framework the results obtained from the correlation coefficients between the ensemble mean prediction of the ECMWF System 4 and the observed wind speeds are compared with the results from the correlations between the wind speed constructed from the seasonal predicted El Niño index and the observations. An improvement of the skill at lead times ranging from 1 up to 5 months is measured over several regions such as Northern United States, Canada, Uruguay and Argentina. The added value of this constructed wind speed predictions is found in those areas over the world where the seasonal prediction system is not able to reproduce correctly the teleconnections of El Niño. Buontempo C, Hanlon H.M., Bruno Soares M., Christel I., Soubeyroux J-M., Viel C., Calmanti S, Bosi L., Falloon P., Palin E.J., Vanvyve E., Torralba V., Gonzalez-Reviriego N., Doblas-Reyes F.J., Pope E.C.D., Newton P. and Liggins F., 2016: What have we learnt from EUPORIAS climate service prototypes? Climate Services (Submitted) Torralba V., Doblas-Reyes F.J., Macleod D., Christel I. and Davis M., 2016: Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology (Submitted)

  14. An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

    Science.gov (United States)

    Ranganayaki, V; Deepa, S N

    2016-01-01

    Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

  15. Modeling of the dynamics of wind to power conversion including high wind speed behavior

    DEFF Research Database (Denmark)

    Litong-Palima, Marisciel; Bjerge, Martin Huus; Cutululis, Nicolaos Antonio

    2016-01-01

    This paper proposes and validates an efficient, generic and computationally simple dynamic model for the conversion of the wind speed at hub height into the electrical power by a wind turbine. This proposed wind turbine model was developed as a first step to simulate wind power time series...... speed shutdowns and restarts are represented as on–off switching rules that govern the output of the wind turbine at extreme wind speed conditions. The model uses the concept of equivalent wind speed, estimated from the single point (hub height) wind speed using a second-order dynamic filter...... measurements available from the DONG Energy offshore wind farm Horns Rev 2. Copyright © 2015 John Wiley & Sons, Ltd....

  16. Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

    Science.gov (United States)

    Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

    2009-01-01

    This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

  17. Performance Analysis of an Island Power System Including Wind Turbines Operating under Random Wind Speed

    OpenAIRE

    Meng-Jen Chen; Yu-Chi Wu; Guo-Tsai Liu; Sen-Feng Lin

    2013-01-01

    With continuous rise of oil price, how to develop alternative energy source has become a hot topic around the world. This study discussed the dynamic characteristics of an island power system operating under random wind speed lower than nominal wind speeds of wind turbines. The system primarily consists of three diesel engine power generation systems, three constant-speed variable-pitch wind turbines, a small hydraulic induction generation system, and lumped static loads. Detailed models b...

  18. Pollutant forecasting error based on persistence of wind direction

    International Nuclear Information System (INIS)

    Cooper, R.E.

    1978-01-01

    The purpose of this report is to provide a means of estimating the reliability of forecasts of downwind pollutant concentrations from atmospheric puff releases. These forecasts are based on assuming the persistence of wind direction as determined at the time of release. This initial forecast will be used to deploy survey teams, to predict population centers that may be affected, and to estimate the amount of time available for emergency response. Reliability of forecasting is evaluated by developing a cumulative probability distribution of error as a function of lapsed time following an assumed release. The cumulative error is determined by comparing the forecast pollutant concentration with the concentration measured by sampling along the real-time meteorological trajectory. It may be concluded that the assumption of meteorological persistence for emergency response is not very good for periods longer than 3 hours. Even within this period, the possibiity for large error exists due to wind direction shifts. These shifts could affect population areas totally different from those areas first indicated

  19. Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting

    Directory of Open Access Journals (Sweden)

    Konstantinos N. Genikomsakis

    2017-11-01

    Full Text Available The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.

  20. Scaling forecast models for wind turbulence and wind turbine power intermittency

    Science.gov (United States)

    Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

    2017-04-01

    The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

  1. Wind speed and wind power short and medium range predictions for complex terrain using artificial neural networks and ensemble calibration

    Science.gov (United States)

    Schicker, Irene; Papazek, Petrina; Kann, Alexander; Wang, Yong

    2017-04-01

    Reliable predictions of wind speed and wind power are vital for balancing the electricity network. Within the last two decades the amount of energy stemming from renewable sources increased substantially relying heavily on the prevailing synoptic conditions. Especially for regions with complex terrain and forested surfaces providing reliable predictions is a challenging task. Forecasts in the nowcasting as well as in the (two) day-ahead range are thus essential for the network balancing. Predictions of wind speed and wind power from the nowcasting to the +72-hour forecast range using NWP models in regions with complex terrain need a suitable horizontal, vertical and temporal resolution (e.g. 10 - 15 minute forecasts for the Nowcasting range) requiring high performance computing. To be able to provide sub-hourly to hourly forecasts different approaches such as model output statistics (MOS) or artificial neural networks (ANN) - including feed forward recurrent neural networks, fuzzy logic, particle swarm optimizations - are needed as computational costs are too high. To represent the forecast uncertainties additional probabilistic ensemble predictions are required increasing the computational needs. Ensemble prediction systems account for errors and uncertainties in the initial and boundary conditions, parameterizations, numeric, etc. Due to the underestimation of model and sampling errors ensemble predictions tend to be underdispersive and biased. They lack, too, sharpness and reliability. These shortcomings can be accounted for using statistical post-processing methods such as the non-homogeneous Gaussian regression (NGR) to calibrate an ensemble. These calibrated ensembles provide forecasts in the medium range for any arbitrary location where observations are available. In this study an ANN is used to provide forecasts for the nowcasting and medium-range with sub-hourly to hourly predictions for different Austrian sites, including high alpine sites as well as low

  2. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time......-varying regression model. In a second step, time-series models, i.e., ARMA and Holt–Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two...

  3. Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

    International Nuclear Information System (INIS)

    Zhong Jian; Dong Gang; Sun Yimei; Zhang Zhaoyang; Wu Yuqin

    2016-01-01

    The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. (paper)

  4. Association Between the Solar Wind Speed, Interplanetary Magnetic ...

    Indian Academy of Sciences (India)

    Meena Pokharia

    2017-11-27

    Nov 27, 2017 ... Abstract. The purpose of the present study is to investigate the association of the cosmic ray intensity (CRI) and interplanetary magnetic field (IMF) with high speed solar wind streams (HSSWS) and slow speed solar wind streams (SSSWS) for solar cycle −23 and 24. We have found very interesting and ...

  5. Temperature variability, intensity of wind speed and visibility during ...

    African Journals Online (AJOL)

    The study assessed the connections between temperature variability, intensity of wind speed and their effect on visibility of Makurdi town during the harmattan season. Data on mean monthly temperature of harmattan months of November, December, January and February (2001 to 2011), Wind speed and visibility records ...

  6. Association Between the Solar Wind Speed, Interplanetary Magnetic ...

    Indian Academy of Sciences (India)

    The purpose of the present study is to investigate the association of the cosmic ray intensity (CRI) and interplanetary magnetic field (IMF) with high speed solar wind streams (HSSWS) and slow speed solar wind streams (SSSWS) for solar cycle −23 and 24. We have found very interesting and adequate results where CRI ...

  7. Power curve report - with rotor equivalent wind speed

    DEFF Research Database (Denmark)

    Villanueva, Héctor; Gómez Arranz, Paula

    , the reference wind speed used in the power curve is the equivalent wind speed obtained from lidar measurements at several heights between lower and upper blade tip, in combination with a hub height meteorological mast. The measurements have been performed using DTU’s measurement equipment, the analysis...

  8. Rotor equivalent wind speed for power curve measurement – comparative exercise for IEA Wind Annex 32

    DEFF Research Database (Denmark)

    Wagner, Rozenn; Cañadillas, B.; Clifton, A.

    2014-01-01

    A comparative exercise has been organised within the International Energy Agency (IEA) Wind Annex 32 in order to test the Rotor Equivalent Wind Speed (REWS) method under various conditions of wind shear and measurement techniques. Eight organisations from five countries participated in the exercise....... Each member of the group has derived both the power curve based on the wind speed at hub height and the power curve based on the REWS. This yielded results for different wind turbines, located in diverse types of terrain and where the wind speed profile was measured with different instruments (mast...... was the definition of the segment area used as weighting for the wind speeds measured at the various heights in the calculation of the REWS. This comparative exercise showed that the REWS method results in a significant difference compared to the standard method using the wind speed at hub height in conditions...

  9. Enhanced short-term wind power forecasting and value to grid operations. The wind forecasting improvement project

    Energy Technology Data Exchange (ETDEWEB)

    Orwig, Kirsten D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States). Transmission Grid Integration; Benjamin, Stan; Wilczak, James; Marquis, Melinda [National Oceanic and Atmospheric Administration, Boulder, CO (United States). Earth System Research Lab.; Stern, Andrew [National Oceanic and Atmospheric Administration, Silver Spring, MD (United States); Clark, Charlton; Cline, Joel [U.S. Department of Energy, Washington, DC (United States). Wind and Water Power Program; Finley, Catherine [WindLogics, Grand Rapids, MN (United States); Freedman, Jeffrey [AWS Truepower, Albany, NY (United States)

    2012-07-01

    The current state-of-the-art wind power forecasting in the 0- to 6-h timeframe has levels of uncertainty that are adding increased costs and risks to the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: (1) a one-year field measurement campaign within two regions; (2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and (3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provide an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis. (orig.)

  10. The influence of the Wind Speed Profile on Wind Turbine Performance Measurements

    DEFF Research Database (Denmark)

    Wagner, Rozenn; Antoniou, Ioannis; Pedersen, Søren M.

    2009-01-01

    . Assuming a certain turbine hub height, the profiles with hub-height wind speeds between 6 m s-1 and 8 m s-1 are normalized at 7 m s-1 and grouped to a number of mean shear profiles. The energy in the profiles varies considerably for the same hub-height wind speed. These profiles are then used as input...... the swept rotor area would allow the determination of the electrical power as a function of an equivalent wind speed where wind shear and turbulence intensity are taken into account. Electrical power is found to correlate significantly better to the equivalent wind speed than to the single point hub...

  11. Learning to forecast wind at remote sites for wind energy applications. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Notis, C.; Trettel, D.W.; Aquino, J.T.; Piazza, T.R.; Taylor, L.E.; Trask, D.C.; Wegley, H.L.; Miller, A.H.

    1983-01-01

    Observed wind patterns are correlated with synoptic or mescoscale weather systems. Six sites selected for analysis include Montauk Point, New York; Boone, North Carolina; Ludington, Michigan; Clayton, New Mexico; Amarillo, Texas; and San Gorgonio Pass, California. Objectives of the analysis are: to identify synoptic and/or mesoscale weather patterns that are associated with recognizable wind events at the sites; to define a set of criteria that uniquely describes such weather patterns; to estimate the reliability (accuracy) of forecasting rules derived from the association of weather patterns and site winds; and to attempt to separate any mesoscale effects of local topography from the synoptic-scale effects. One-to-one mapping of wind regimes onto synoptic types was not found. It was concluded that four factors should be examined when stratifying wind regimes: synoptic situation, descriptive climatology, pressure gradient vector, and winds aloft. The wind forecasting approach developed was intended for forecasting hourly average winds out to the 24 hour or possibly 36 hour time horizon. (LEW)

  12. Actuator disk model of wind farms based on the rotor average wind speed

    DEFF Research Database (Denmark)

    Han, Xing Xing; Xu, Chang; Liu, De You

    2016-01-01

    Due to difficulty of estimating the reference wind speed for wake modeling in wind farm, this paper proposes a new method to calculate the momentum source based on the rotor average wind speed. The proposed model applies volume correction factor to reduce the influence of the mesh recognition...

  13. Assessment of C-Type Darrieus Wind Turbine Under Low Wind Speed Condition

    Science.gov (United States)

    Misaran, M. S.; Rahman, Md. M.; Muzammil, W. K.; Ismail, M. A.

    2017-07-01

    Harvesting wind energy in in a low wind speed region is deem un-economical if not daunting task. Study shows that a minimum cut in speed of 3.5 m/s is required to extract a meaningful wind energy for electricity while a mean speed of 6 m/s is preferred. However, in Malaysia the mean speed is at 2 m/s with certain potential areas having 3 m/s mean speed. Thus, this work aims to develop a wind turbine that able to operate at lower cut-in speed and produce meaningful power for electricity generation. A C-type Darrieus blade is selected as it shows good potential to operate in arbitrary wind speed condition. The wind turbine is designed and fabricated in UMS labs while the performance of the wind turbine is evaluated in a simulated wind condition. Test result shows that the wind turbine started to rotate at 1 m/s compared to a NACA 0012 Darrieus turbine that started to rotate at 3 m/s. The performance of the turbine shows that it have good potential to be used in an intermittent arbitrary wind speed condition as well as low mean wind speed condition.

  14. A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

    Directory of Open Access Journals (Sweden)

    Henrik Madsen

    2012-03-01

    Full Text Available Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i the modeling of a nonlinear and non-stationary stochastic process; (ii the practical implementation of the model we proposed; (iii the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts.

  15. Statistical Short-Range Guidance for Peak Wind Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station, Phase III

    Science.gov (United States)

    Crawford, Winifred

    2010-01-01

    This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations.The tool includes climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  16. Demand forecast of turbines in the offshore wind power industry

    DEFF Research Database (Denmark)

    Martinez-Neri, Ivan

    2014-01-01

    How important is it for a manufacturing company to be able to predict the demand of their products? How much will it lose in inventory costs due to a bad forecasting technique? And what if the product in question is composed of more than 100,000 parts and costs millions of euros a piece? This art......? This article summarises the reasoning followed by a European manufacturer to determine the demand curve of finished offshore wind turbines and how to forecast it for the purpose of production planning.......How important is it for a manufacturing company to be able to predict the demand of their products? How much will it lose in inventory costs due to a bad forecasting technique? And what if the product in question is composed of more than 100,000 parts and costs millions of euros a piece...

  17. Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Madsen, Henrik; Nielsen, Torben Skov

    2006-01-01

    speed (due to the non-linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field...

  18. Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations.

    Science.gov (United States)

    Ángel Prósper Fernández, Miguel; Casal, Carlos Otero; Canoura Fernández, Felipe; Miguez-Macho, Gonzalo

    2017-04-01

    Regional meteorological models are becoming a generalized tool for forecasting wind resource, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the production forecast and validation of a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex terrain region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data provided by the management company. Results show that WRF is able to yield good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions.

  19. A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data

    DEFF Research Database (Denmark)

    Alessandrini, S.; Sperati, S.; Pinson, Pierre

    2013-01-01

    within COnsortium for Small-scale MOdelling) applied for power forecasts on a real case in Southern Italy is presented. The approach is based on retrieving meteorological ensemble variables (i.e. wind speed, wind direction), using them to create a power Probability Density Function (PDF) for each 0-72. h......Wind power forecasting (WPF) represents a crucial tool to reduce problems of grid integration and to facilitate energy trading. By now it is advantageous to associate a deterministic forecast with a probabilistic one, in order to give to the end-users information about prediction uncertainty...

  20. Transient analysis of variable-speed wind turbines at wind speed disturbances and a pitch control malfunction

    International Nuclear Information System (INIS)

    Melicio, R.; Mendes, V.M.F.; Catalao, J.P.S.

    2011-01-01

    As wind power generation undergoes rapid growth, new technical challenges emerge: dynamic stability and power quality. The influence of wind speed disturbances and a pitch control malfunction on the quality of the energy injected into the electric grid is studied for variable-speed wind turbines with different power-electronic converter topologies. Additionally, a new control strategy is proposed for the variable-speed operation of wind turbines with permanent magnet synchronous generators. The performance of disturbance attenuation and system robustness is ascertained. Simulation results are presented and conclusions are duly drawn.

  1. Validation of Model Forecasts of the Ambient Solar Wind

    Science.gov (United States)

    Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

    2009-01-01

    Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

  2. AE Geomagnetic Index Predictability for High Speed Solar Wind Streams: A Wavelet Decomposition Technique

    Science.gov (United States)

    Guarnieri, Fernando L.; Tsurutani, Bruce T.; Hajra, Rajkumar; Echer, Ezequiel; Gonzalez, Walter D.; Mannucci, Anthony J.

    2014-01-01

    High speed solar wind streams cause geomagnetic activity at Earth. In this study we have applied a wavelet interactive filtering and reconstruction technique on the solar wind magnetic field components and AE index series to allowed us to investigate the relationship between the two. The IMF Bz component was found as the most significant solar wind parameter responsible by the control of the AE activity. Assuming magnetic reconnection associated to southward directed Bz is the main mechanism transferring energy into the magnetosphere, we adjust parameters to forecast the AE index. The adjusted routine is able to forecast AE, based only on the Bz measured at the L1 Lagrangian point. This gives a prediction approximately 30-70 minutes in advance of the actual geomagnetic activity. The correlation coefficient between the observed AE data and the forecasted series reached values higher than 0.90. In some cases the forecast reproduced particularities observed in the signal very well.The high correlation values observed and the high efficacy of the forecasting can be taken as a confirmation that reconnection is the main physical mechanism responsible for the energy transfer during HILDCAAs. The study also shows that the IMF Bz component low frequencies are most important for AE prediction.

  3. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    2014-01-01

    For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient integration into the power grid. Increased forecast precision allows end-users to plan day-ahead operation with reduced risk of penalties which in turn supports the feasibility of wind...... energy. The present study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by downscaling global numerical weather prediction (NWP) data from the National Centers for Environmental Prediction Global Forecast System (GFS) using the limited-area NWP model described...

  4. Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

    Energy Technology Data Exchange (ETDEWEB)

    Optis, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Scott, George N. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Draxl, Caroline [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2018-02-02

    The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present. Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.

  5. A high speed vertical axis wind machine

    National Research Council Canada - National Science Library

    South, P

    1976-01-01

    The operational feasibility of vertical axis wind machines was investigated at the National Aeronautical Establishment in Ottawa through use of a wind tunnel and a rotor with blades curved in a skipping rope shape...

  6. Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

    DEFF Research Database (Denmark)

    Bremnes, John Bjørnar; Giebel, Gregor

    2017-01-01

    In most operational wind power forecasting systems statistical methods are applied to map wind forecasts from numerical weather prediction (NWP) models into wind power forecasts. NWP models are complex mathematical models of the atmosphere that divide the earth’s surface into a grid. The spatial...... resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

  7. Acoustic and wind speed data analysis as an environmental issue

    International Nuclear Information System (INIS)

    Whitson, R.J.; MacKinnon, A.

    1995-01-01

    This paper examines how the output from a cup anemometer, used for wind speed measurement, can be recorded on magnetic tape and analysed using instrumentation normally employed to measure acoustic data. The purpose of this being to allow true simultaneous analysis of acoustic and wind speed data. NEL's NWTC (National Wind Turbine Centre) Anemometer Calibration Facility is used to compare pulsed and analogue outputs from a typical anemometer to the data obtained from a pitot/static tube for a range of different wind speeds. The usefulness of 1/24- and 1/12-octave analysis is examined and accuracy limits are derived for the 'acoustic' approach to wind speed measurement. The allowable positions for anemometer locations are also discussed with reference to currently available standards and recommended practices. (Author)

  8. Doubly Fed Drives for Variable Speed Wind Turbines

    DEFF Research Database (Denmark)

    Lindholm, Morten

    2004-01-01

    This thesis deals with the use of variable speed wind turbines. Different wind turbine generator topologies are described. In particular, the reduced variable speed turbine, which uses a doubly fed induction generator, is covered. An overview of the power electronic inverters of interest to the f...... is constructed. Adaptive active flters are used to reduce harmonics and slip harmonics in the stator current. The flters are implemented in both inverters. The active flters reduce the stator harmonics by 20-30 dB. The flters can reduce the slip harmonics at variable speed.......This thesis deals with the use of variable speed wind turbines. Different wind turbine generator topologies are described. In particular, the reduced variable speed turbine, which uses a doubly fed induction generator, is covered. An overview of the power electronic inverters of interest...

  9. Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region

    International Nuclear Information System (INIS)

    Ozay, Can; Celiktas, Melih Soner

    2016-01-01

    Highlights: • Wind speed & direction data from September 2008 to March 2014 has been analyzed. • Mean wind speed for the whole data set has been found to be 8.11 m/s. • Highest wind speed is observed in July with a monthly mean value of 9.10 m/s. • Wind speed with the most energy has been calculated as 12.77 m/s. • Observed data has been fit to a Weibull distribution and k &c parameters have been calculated as 2.05 and 9.16. - Abstract: Weibull Statistical Distribution is a common method for analyzing wind speed measurements and determining wind energy potential. Weibull probability density function can be used to forecast wind speed, wind density and wind energy potential. In this study a two-parameter Weibull statistical distribution is used to analyze the wind characteristics of Alaçatı region, located in Çeşme, İzmir. The data used in the density function are acquired from a wind measurement station in Alaçatı. Measurements were gathered on three different heights respectively 70, 50 and 30 m between 10 min intervals for five and half years. As a result of this study; wind speed frequency distribution, wind direction trends, mean wind speed, and the shape and the scale (k&c) Weibull parameters have been calculated for the region. Mean wind speed for the entirety of the data set is found to be 8.11 m/s. k&c parameters are found as 2.05 and 9.16 in relative order. Wind direction analysis along with a wind rose graph for the region is also provided with the study. Analysis suggests that higher wind speeds which range from 6–12 m/s are prevalent between the sectors 340–360°. Lower wind speeds, from 3 to 6 m/s occur between sectors 10–29°. Results of this study contribute to the general knowledge about the regions wind energy potential and can be used as a source for investors and academics.

  10. Ocean Surface Wind Speed of Hurricane Helene Observed by SAR

    DEFF Research Database (Denmark)

    Xu, Qing; Cheng, Yongcun; Li, Xiaofeng

    2011-01-01

    The hurricanes can be detected by many remote sensors, but synthetic aperture radar (SAR) can yield high-resolution (sub-kilometer) and low-level wind information that cannot be seen below the cloud by other sensors. In this paper, an assessment of SAR capability of monitoring high......-resolution hurricane was conducted. A case study was carried out to retrieve ocean surface wind field from C-band RADARSAT-1 SAR image which captured the structure of hurricane Helene over the Atlantic Ocean on 20 September, 2006. With wind direction from the outputs of U.S. Navy Operational Global Atmospheric...... CIWRAP models have been tested to extract wind speed from SAR data. The SAR retrieved ocean surface winds were compared to the aircraft wind speed observations from stepped frequency microwave radiometer (SFMR). The results show the capability of hurricane wind monitoring by SAR....

  11. Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model

    Directory of Open Access Journals (Sweden)

    Shuting Wan

    2015-06-01

    Full Text Available Natural wind is stochastic, being characterized by its speed and direction which change randomly and frequently. Because of the certain lag in control systems and the yaw body itself, wind turbines cannot be accurately aligned toward the wind direction when the wind speed and wind direction change frequently. Thus, wind turbines often suffer from a series of engineering issues during operation, including frequent yaw, vibration overruns and downtime. This paper aims to study the effects of yaw error on wind turbine running characteristics at different wind speeds and control stages by establishing a wind turbine model, yaw error model and the equivalent wind speed model that includes the wind shear and tower shadow effects. Formulas for the relevant effect coefficients Tc, Sc and Pc were derived. The simulation results indicate that the effects of the aerodynamic torque, rotor speed and power output due to yaw error at different running stages are different and that the effect rules for each coefficient are not identical when the yaw error varies. These results may provide theoretical support for optimizing the yaw control strategies for each stage to increase the running stability of wind turbines and the utilization rate of wind energy.

  12. A joint probability density function of wind speed and direction for wind energy analysis

    International Nuclear Information System (INIS)

    Carta, Jose A.; Ramirez, Penelope; Bueno, Celia

    2008-01-01

    A very flexible joint probability density function of wind speed and direction is presented in this paper for use in wind energy analysis. A method that enables angular-linear distributions to be obtained with specified marginal distributions has been used for this purpose. For the marginal distribution of wind speed we use a singly truncated from below Normal-Weibull mixture distribution. The marginal distribution of wind direction comprises a finite mixture of von Mises distributions. The proposed model is applied in this paper to wind direction and wind speed hourly data recorded at several weather stations located in the Canary Islands (Spain). The suitability of the distributions is judged from the coefficient of determination R 2 . The conclusions reached are that the joint distribution proposed in this paper: (a) can represent unimodal, bimodal and bitangential wind speed frequency distributions, (b) takes into account the frequency of null winds, (c) represents the wind direction regimes in zones with several modes or prevailing wind directions, (d) takes into account the correlation between wind speeds and its directions. It can therefore be used in several tasks involved in the evaluation process of the wind resources available at a potential site. We also conclude that, in the case of the Canary Islands, the proposed model provides better fits in all the cases analysed than those obtained with the models used in the specialised literature on wind energy

  13. Probability distribution of surface wind speed induced by convective adjustment on Venus

    Science.gov (United States)

    Yamamoto, Masaru

    2017-03-01

    The influence of convective adjustment on the spatial structure of Venusian surface wind and probability distribution of its wind speed is investigated using an idealized weather research and forecasting model. When the initially uniform wind is much weaker than the convective wind, patches of both prograde and retrograde winds with scales of a few kilometers are formed during active convective adjustment. After the active convective adjustment, because the small-scale convective cells and their related vertical momentum fluxes dissipate quickly, the large-scale (>4 km) prograde and retrograde wind patches remain on the surface and in the longitude-height cross-section. This suggests the coexistence of local prograde and retrograde flows, which may correspond to those observed by Pioneer Venus below 10 km altitude. The probability distributions of surface wind speed V during the convective adjustment have a similar form in different simulations, with a sharp peak around ∼0.1 m s-1 and a bulge developing on the flank of the probability distribution. This flank bulge is associated with the most active convection, which has a probability distribution with a peak at the wind speed 1.5-times greater than the Weibull fitting parameter c during the convective adjustment. The Weibull distribution P(> V) (= exp[-(V/c)k]) with best-estimate coefficients of Lorenz (2016) is reproduced during convective adjustments induced by a potential energy of ∼7 × 107 J m-2, which is calculated from the difference in total potential energy between initially unstable and neutral states. The maximum vertical convective heat flux magnitude is proportional to the potential energy of the convective adjustment in the experiments with the initial unstable-layer thickness altered. The present work suggests that convective adjustment is a promising process for producing the wind structure with occasionally generating surface winds of ∼1 m s-1 and retrograde wind patches.

  14. Wind Speed Influences on Marine Aerosol Optical Depth

    Directory of Open Access Journals (Sweden)

    Colin O'Dowd

    2010-01-01

    Full Text Available The Mulcahy (Mulcahy et al., 2008 power-law parameterization, derived at the coastal Atlantic station Mace Head, between clean marine aerosol optical depth (AOD and wind speed is compared to open ocean MODIS-derived AOD versus wind speed. The reported AOD versus wind speed (U was a function of ∼U2. The open ocean MODIS-derived AOD at 550 nm and 860 nm wavelengths, while in good agreement with the general magnitude of the Mulcahy parameterization, follows a power-law with the exponent ranging from 0.72 to 2.47 for a wind speed range of 2–18 m s−1. For the four cases examined, some MODIS cases underestimated AOD while other cases overestimated AOD relative to the Mulcahy scheme. Overall, the results from MODIS support the general power-law relationship of Mulcahy, although some linear cases were also encountered in the MODIS dataset. Deviations also arise between MODIS and Mulcahy at higher wind speeds (>15 m s−1, where MODIS-derived AOD returns lower values as compared to Mulcahy. The results also support the suggestion than wind generated sea spray, under moderately high winds, can rival anthropogenic pollution plumes advecting out into marine environments with wind driven AOD contributing to AOD values approaching 0.3.

  15. Algorithm for wind speed estimate with polarimetric radar

    Directory of Open Access Journals (Sweden)

    Ю. А. Авер’янова

    2013-07-01

    Full Text Available The connection of wind speed and drops behavior is substantiated as well as the drop behavior influence onto the polarization characteristics of electromagnetic waves. The expression to calculate the wind speed taking into account the Weber number for the critical regime of drop deformation is obtained. The critical regime of drop deformation is the regime when drop is divided into two parts. The dependency of critical wind speed on the drop diameter is calculated and shown. The concept o polarization spectrum that is introduced in the previous papers is used to estimate the dynamic processes in the atmosphere. At the moment when the drop is under the influence of the wind that is equal to the critical wind speed the drop will be divided into two parts. This process will be reflected as the appearance of the two equal components of polarization spectra of reflected electromagnetic waves at the orthogonal antennas of Doppler Polarimetric Radar. Owing the information about the correspondence of the polarization component energy level to the drop diameter it is possible to estimate the wind speed with the obtained dependency. The process of the wind speed estimate with polarimetric radar is presented with the developed common algorithm

  16. Critical Speed Control for a Fixed Blade Variable Speed Wind Turbine

    Directory of Open Access Journals (Sweden)

    Morgan Rossander

    2017-10-01

    Full Text Available A critical speed controller for avoiding a certain rotational speed is presented. The controller is useful for variable speed wind turbines with a natural frequency in the operating range. The controller has been simulated, implemented and tested on an open site 12 kW vertical axis wind turbine prototype. The controller is based on an adaptation of the optimum torque control. Two lookup tables and a simple state machine provide the control logic of the controller. The controller requires low computational resources, and no wind speed measurement is needed. The results suggest that the controller is a feasible method for critical speed control. The skipping behavior can be adjusted using only two parameters. While tested on a vertical axis wind turbine, it may be used on any variable speed turbine with the control of generator power.

  17. Assimilation of wind speed and direction observations: results from real observation experiments

    Directory of Open Access Journals (Sweden)

    Feng Gao

    2015-06-01

    Full Text Available The assimilation of wind observations in the form of speed and direction (asm_sd by the Weather Research and Forecasting Model Data Assimilation System (WRFDA was performed using real data and employing a series of cycling assimilation experiments for a 2-week period, as a follow-up for an idealised post hoc assimilation experiment. The satellite-derived Atmospheric Motion Vectors (AMV and surface dataset in Meteorological Assimilation Data Ingest System (MADIS were assimilated. This new method takes into account the observation errors of both wind speed (spd and direction (dir, and WRFDA background quality control (BKG-QC influences the choice of wind observations, due to data conversions between (u,v and (spd, dir. The impacts of BKG-QC, as well as the new method, on the wind analysis were analysed separately. Because the dir observational errors produced by different platforms are not known or tuned well in WRFDA, a practical method, which uses similar assimilation weights in comparative trials, was employed to estimate the spd and dir observation errors. The asm_sd produces positive impacts on analyses and short-range forecasts of spd and dir with smaller root-mean-square errors than the u,v-based system. The bias of spd analysis decreases by 54.8%. These improvements result partly from BKG-QC screening of spd and dir observations in a direct way, but mainly from the independent impact of spd (dir data assimilation on spd (dir analysis, which is the primary distinction from the standard WRFDA method. The potential impacts of asm_sd on precipitation forecasts were evaluated. Results demonstrate that the asm_sd is able to indirectly improve the precipitation forecasts by improving the prediction accuracies of key wind-related factors leading to precipitation (e.g. warm moist advection and frontogenesis.

  18. Economic performance indicators of wind energy based on wind speed stochastic modeling

    International Nuclear Information System (INIS)

    D’Amico, Guglielmo; Petroni, Filippo; Prattico, Flavio

    2015-01-01

    Highlights: • We propose a new and different wind energy production indicator. • We compute financial profitability of potential wind power sites. • The wind speed process is modeled as an indexed semi-Markov chain. • We check if the wind energy is a good investment with and without incentives. - Abstract: We propose the computation of different wind energy production indicators and financial profitability of potential wind power sites. The computation is performed by modeling the wind speed process as an indexed semi-Markov chain to predict and simulate the wind speed dynamics. We demonstrate that the indexed semi-Markov chain approach enables reproducing the indicators calculated on real data. Two different time horizons of 15 and 30 years are analyzed. In the first case we consider the government incentives on the energy price now present in Italy, while in the second case the incentives have not been taken into account

  19. VT Predicted Mean Wind Speed - 30 meter height

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) Wind speed predictions at 30m are generated by a numerical model that simulates weather conditions over a 15-year period, taking into account...

  20. VT Predicted Mean Wind Speed - 70 meter height

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) Wind speed predictions at 70m are generated by a numerical model that simulates weather conditions over a 15-year period, taking into account...

  1. Multisensor satellite data integration for sea surface wind speed and direction determination

    Science.gov (United States)

    Glackin, D. L.; Pihos, G. G.; Wheelock, S. L.

    1984-01-01

    Techniques to integrate meteorological data from various satellite sensors to yield a global measure of sea surface wind speed and direction for input to the Navy's operational weather forecast models were investigated. The sensors were launched or will be launched, specifically the GOES visible and infrared imaging sensor, the Nimbus-7 SMMR, and the DMSP SSM/I instrument. An algorithm for the extrapolation to the sea surface of wind directions as derived from successive GOES cloud images was developed. This wind veering algorithm is relatively simple, accounts for the major physical variables, and seems to represent the best solution that can be found with existing data. An algorithm for the interpolation of the scattered observed data to a common geographical grid was implemented. The algorithm is based on a combination of inverse distance weighting and trend surface fitting, and is suited to combing wind data from disparate sources.

  2. Validation of wind speed prediction methods at offshore sites

    Science.gov (United States)

    McQueen, Dougal; Watson, Simon

    2006-01-01

    As ever more offshore sites are being investigated for the installation of wind farms, there is a need for accurate estimates of the long-term mean wind speeds at these sites. The cost of installing masts at offshore sites is high compared with onshore sites. In the short term this cost may be difficult to avoid. However, if a developer could get an estimate of the expected long-term mean wind speed at a potential offshore site using available onshore data sets, this could at least inform the choice of site for more advanced monitoring. With this in mind we present the results of using a number of simple standard analyses to infer the wind speed at three UK offshore masts and a mast and lighthouse off the coast of Ireland. Onshore surface wind measurements, upper air measurements, numerical weather prediction model output pressure data on a regular grid, wind speed output from two numerical weather prediction models and reanalysis data are transformed to the sites of interest using relatively simple methods neglecting the effect of topography or a roughness change-induced internal boundary layer and assuming neutral atmospheric stability. The predicted wind speeds are compared with actual measurements at the offshore masts and any discrepancies are assessed and discussed. Copyright

  3. Measured wind speed trends on the west coast of Canada

    Science.gov (United States)

    Tuller, Stanton E.

    2004-09-01

    Trends in measured wind speed are discussed for four stations on the west coast of Canada. Periods of record vary with the station. They begin in the late 1940s or the 1950s and run through to the early to mid 1990s. The most prominent feature of the time series was a decline in mean annual and winter wind speeds at Cape St James, Victoria International Airport, and Vancouver International Airport during the middle portion of the record. Declines in mean annual wind speed are matched by increases in the percentage of calms and decreases in high wind speed observations. The pressure gradient between Victoria, Vancouver and Comox, the Pacific North American index, the Pacific decadal oscillation index, and other climate elements in British Columbia and the northwestern USA show trends at roughly the same time, indicating a natural cause of the wind speed decrease. Comox Airport mean wind speeds increased, however, perhaps the result of reduced friction in the vicinity of the anemometer outweighing the decrease in the regional pressure gradient.

  4. Wind speed power spectrum analysis for Bushland, Texas

    Energy Technology Data Exchange (ETDEWEB)

    Eggleston, E.D. [USDA-Agricultural Research Service, Bushland, TX (United States)

    1996-12-31

    Numerous papers and publications on wind turbulence have referenced the wind speed spectrum presented by Isaac Van der Hoven in his article entitled Power Spectrum of Horizontal Wind Speed Spectrum in the Frequency Range from 0.0007 to 900 Cycles per Hour. Van der Hoven used data measured at different heights between 91 and 125 meters above the ground, and represented the high frequency end of the spectrum with data from the peak hour of hurricane Connie. These facts suggest we should question the use of his power spectrum in the wind industry. During the USDA - Agricultural Research Service`s investigation of wind/diesel system power storage, using the appropriate wind speed power spectrum became a significant issue. We developed a power spectrum from 13 years of hourly average data, 1 year of 5 minute average data, and 2 particularly gusty day`s 1 second average data all collected at a height of 10 meters. While the general shape is similar to the Van der Hoven spectrum, few of his peaks were found in the Bushland spectrum. While higher average wind speeds tend to suggest higher amplitudes in the high frequency end of the spectrum, this is not always true. Also, the high frequency end of the spectrum is not accurately described by simple wind statistics such as standard deviation and turbulence intensity. 2 refs., 5 figs., 1 tab.

  5. Statistical analysis of wind speed for electrical power generation in ...

    African Journals Online (AJOL)

    Also, the results have shown that Jos, Kano and Minna fall in class 4 and therefore suitable for both off grid and grid connected modes. In addition, the effects of c and k parameters on the probability distribution functions have been presented. Keywords: Wind speed - probability - density function – wind energy conversion ...

  6. Decomposition of wind speed fluctuations at different time scales

    Indian Academy of Sciences (India)

    Understanding the inherent features of wind speed (variability on different time scales) has become critical for assured wind power availability, grid stability, and effective power management. The study utilizes the wavelet, autocorrelation, and FFT (fast Fourier transform) techniques to analyze and assimilate the fluctuating ...

  7. Representivity of wind measurements for design wind speed estimations

    CSIR Research Space (South Africa)

    Goliger, Adam M

    2013-07-01

    Full Text Available Engineer in South Africa, January 1987. Wever, N., and G. Groen. 2009. Improving potential wind for extreme wind statistics. KNMI scientific report - wetenschappelijk rapport : WR 2009-02. KNMI. De Bilt. The Netherlands. 114 pp. Wieringa, J. 1986...

  8. Design of a Small Scale Wind Generator for Low Wind Speed Areas ...

    African Journals Online (AJOL)

    Most small scale level wind turbine generators are directly driven system, variable speed, and partially connected power electronic converter system. Choice of such system is to avoid costs associated with gearbox. However, due to low wind speed in most of the tropical countries, synchronous generators with smaller or ...

  9. Adaptive Torque Control of Variable Speed Wind Turbines

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, K. E.

    2004-08-01

    The primary focus of this work is a new adaptive controller that is designed to resemble the standard non-adaptive controller used by the wind industry for variable speed wind turbines below rated power. This adaptive controller uses a simple, highly intuitive gain adaptation law designed to seek out the optimal gain for maximizing the turbine's energy capture. It is designed to work even in real, time-varying winds.

  10. Prediction of wind power potential by wind speed probability distribution in a hilly terrain near Bh

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed, Siraj; Diwakar, Nilesh

    2010-09-15

    Daily wind speed data in metre per second and its direction of flow in degree were recorded from of the India Meteorological Department for a site near the Bhopal Airport for the period of eleven years. The influence of roughness of the terrain, obstacles and topography in terms of contour for the area were also taken into consideration. These data were analysed using WAsP programme and regional wind climate of the area was determined. It is seen from the analysis of the wind speed data and keeping the topographical variation of terrain, exploitable wind speed is experienced at 50 m.

  11. Thermal loading of wind power converter considering dynamics of wind speed

    DEFF Research Database (Denmark)

    Baygildina, Elvira; Peltoniemi, Pasi; Pyrhönen, Olli

    2013-01-01

    ), and the thermal stress of power devices is investigated from the frequency spectrum point of view of wind speed. It is concluded that because of the strong inertia effects by the aerodynamic behavior of wind turbines, thermal stress of the semiconductors is relatively more stable and only influenced by the low......The thermal loading of power semiconductors is a crucial performance related to the reliability and cost of the wind power converter. However, the thermal loading impacts by the variation of wind speeds have not yet been clarified, especially when considering the aerodynamic behavior of the wind...

  12. Wind power forecasting : state-of-the-art 2009.

    Energy Technology Data Exchange (ETDEWEB)

    Monteiro, C.; Bessa, R.; Miranda, V.; Botterud, A.; Wang, J.; Conzelmann, G.; Decision and Information Sciences; INESC Porto

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and

  13. Wind-stilling in the light of wind speed measurements: the Czech experience

    Czech Academy of Sciences Publication Activity Database

    Brázdil, Rudolf; Valík, A.; Zahradníček, Pavel; Řezníčková, Ladislava; Tolasz, R.; Možný, M.

    2018-01-01

    Roč. 74 (2018), s. 131-143 ISSN 0936-577X R&D Projects: GA MŠk(CZ) LO1415; GA ČR(CZ) GA15-11805S Institutional support: RVO:86652079 Keywords : universal anemograph * vaisala wind-speed sensors * wind speed * homogenisation * wind stilling * Czech Republic Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 1.578, year: 2016

  14. Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2016-08-01

    Full Text Available The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS global all-weather product and stepped-frequency microwave radiometer (SFMR measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain.

  15. An Analysis of Peak Wind Speed Data from Collocated Mechanical and Ultrasonic Anemometers

    Science.gov (United States)

    Short, David A.; Wells, Leonard; Merceret, Francis J.; Roeder, William P.

    2007-01-01

    This study compared peak wind speeds reported by mechanical and ultrasonic anemometers at Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC) on the east central coast of Florida and Vandenberg Air Force Base (VAFB) on the central coast of California. Launch Weather Officers, forecasters, and Range Safety analysts need to understand the performance of wind sensors at CCAFS/KSC and VAFB for weather warnings, watches, advisories, special ground processing operations, launch pad exposure forecasts, user Launch Commit Criteria (LCC) forecasts and evaluations, and toxic dispersion support. The legacy CCAFS/KSC and VAFB weather tower wind instruments are being changed from propeller-and-vane (CCAFS/KSC) and cup-and-vane (VAFB) sensors to ultrasonic sensors under the Range Standardization and Automation (RSA) program. Mechanical and ultrasonic wind measuring techniques are known to cause differences in the statistics of peak wind speed as shown in previous studies. The 45th Weather Squadron (45 WS) and the 30th Weather Squadron (30 WS) requested the Applied Meteorology Unit (AMU) to compare data between the RSA ultrasonic and legacy mechanical sensors to determine if there are significant differences. Note that the instruments were sited outdoors under naturally varying conditions and that this comparison was not designed to verify either technology. Approximately 3 weeks of mechanical and ultrasonic wind data from each range from May and June 2005 were used in this study. The CCAFS/KSC data spanned the full diurnal cycle, while the VAFB data were confined to 1000-1600 local time. The sample of 1-minute data from numerous levels on five different towers on each range totaled more than 500,000 minutes of data (482,979 minutes of data after quality control). The ten towers were instrumented at several levels, ranging from 12 ft to 492 ft above ground level. The ultrasonic sensors were collocated at the same vertical levels as the mechanical sensors and

  16. International wind energy development. World market update 2012. Forecast 2013-2017

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-03-15

    The BTM wind report, World Market Update 2012, published by Navigant Research, is the eighteenth edition of this annual wind energy market report. The report includes more than 80 tables, charts and graphs illustrating global wind market development, as well as a wind market forecast for 2013?2017 and highlighted trends for the wind market through 2022. The report delivers several views on the fast?growing wind market, including: 1) More than 285 GW of wind power now installed globally; 2) 45GW of new capacity added in 2012, including 1.1 GW from offshore wind; 3) The United States surpassed China as the largest market in terms of new installations in 2012; 4) Europe lost its position as the largest world region in terms of new installations; 5) Wind installations in the Americas grew by 12.3 percent compared with 2011; 6) Big shake?up in the top ten wind turbine supplier ranking; 7) Strong Chinese presence among top 15 wind owner?operators; 8) Wind market structures continue to evolve; 9) The penetration of wind power in the world's electricity supply has reached 2.62 percent; 10) Offshore wind more than doubled the capacity added in 2011, with more than 4 GW currently under construction. With the addition of 44,951 MW in new installations in 2012, world wind power capacity grew to around 285,700 MW, an increase in the total wind power installation base of 18.6 percent. Market growth year-over-year in 2012, though a modest 7.8 percent, was still higher than in 2011. Average annual growth for the past five years has been 17.8 percent, achieved during the aftermath of the 2008 financial crisis, with traditionally large markets for wind power in economic recession in America and Europe. The wind power industry continues to demonstrate its ability to rapidly evolve to meet new demands in markets that face a variety of challenges. The focus on product diversification grows with wind turbine vendors designing machines for maximum energy production in low wind speed

  17. Rotor equivalent wind speed for power curve measurement - comparative exercise for IEA Wind Annex 32

    Science.gov (United States)

    Wagner, R.; Cañadillas, B.; Clifton, A.; Feeney, S.; Nygaard, N.; Poodt, M.; St. Martin, C.; Tüxen, E.; Wagenaar, J. W.

    2014-06-01

    A comparative exercise has been organised within the International Energy Agency (IEA) Wind Annex 32 in order to test the Rotor Equivalent Wind Speed (REWS) method under various conditions of wind shear and measurement techniques. Eight organisations from five countries participated in the exercise. Each member of the group has derived both the power curve based on the wind speed at hub height and the power curve based on the REWS. This yielded results for different wind turbines, located in diverse types of terrain and where the wind speed profile was measured with different instruments (mast or various lidars). The participants carried out two preliminary steps in order to reach consensus on how to implement the REWS method. First, they all derived the REWS for one 10 minute wind speed profile. Secondly, they all derived the power curves for one dataset. The main point requiring consensus was the definition of the segment area used as weighting for the wind speeds measured at the various heights in the calculation of the REWS. This comparative exercise showed that the REWS method results in a significant difference compared to the standard method using the wind speed at hub height in conditions with large shear and low turbulence intensity.

  18. Flicker Mitigation by Speed Control of Permanent Magnet Synchronous Generator Variable-Speed Wind Turbines

    Directory of Open Access Journals (Sweden)

    Yanting Hu

    2013-07-01

    Full Text Available Grid-connected wind turbines are fluctuating power sources that may produce flicker during continuous operation. This paper presents a simulation model of a MW-level variable speed wind turbine with a full-scale back-to-back power converter and permanent magnet synchronous generator (PMSG developed in the simulation tool of PSCAD/EMTDC. Flicker emission of this system is investigated. The 3p (three times per revolution power oscillation due to wind shear and tower shadow effects is the significant part in the flicker emission of variable speed wind turbines with PMSG during continuous operation. A new method of flicker mitigation by controlling the rotational speed is proposed. It smoothes the 3p active power oscillations from wind shear and tower shadow effects of the wind turbine by varying the rotational speed of the PMSG. Simulation results show that damping the 3p active power oscillation by using the flicker mitigation speed controller is an effective means for flicker mitigation of variable speed wind turbines with full-scale back-to-back power converters and PMSG during continuous operation.

  19. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient utilisation in the power grid. Statistical wind power prediction tools [1] use numerical weather prediction (NWP) model data along with measurements and can correct magnitude errors op...... the two time series. Results on limited-area NWP model performance, with focus on the 12th to 48th forecast hour horizon relevant for Elspot auction bidding on the Nord Pool Spot market [2], are presented....

  20. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient utilisation in the power grid. Statistical wind power prediction tools [1] use numerical weather prediction (NWP) model data along with measurements and can correct magnitude errors...... the two time series. Results on limited-area NWP model performance, with focus on the 12th to 48th forecast hour horizon relevant for Elspot auction bidding on the Nord Pool Spot market [2], are presented....

  1. Load flow analysis for variable speed offshore wind farms

    DEFF Research Database (Denmark)

    Chen, Zhe; Zhao, Menghua; Blaabjerg, Frede

    2009-01-01

    A serial AC-DC integrated load flow algorithm for variable speed offshore wind farms is proposed. It divides the electrical system of a wind farm into several local networks, and different load flow methods are used for these local networks sequentially. This method is fast, more accurate, and many...... factors such as the different wind farm configurations, the control of wind turbines and the power losses of pulse width modulation converters are considered. The DC/DC converter model is proposed and integrated into load flow algorithm by modifying the Jacobian matrix. Two iterative methods are proposed...... and integrated into the load flow algorithm: one takes into account the control strategy of converters and the other considers the power losses of converters. In addition, different types of variable speed wind turbine systems with different control methods are investigated. Finally, the method is demonstrated...

  2. Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates.

    Science.gov (United States)

    Csavina, Janae; Field, Jason; Félix, Omar; Corral-Avitia, Alba Y; Sáez, A Eduardo; Betterton, Eric A

    2014-07-15

    Atmospheric particulate have deleterious impacts on human health. Predicting dust and aerosol emission and transport would be helpful to reduce harmful impacts but, despite numerous studies, prediction of dust events and contaminant transport in dust remains challenging. In this work, we show that relative humidity and wind speed are both determinants in atmospheric dust concentration. Observations of atmospheric dust concentrations in Green Valley, AZ, USA, and Juárez, Chihuahua, México, show that PM10 concentrations are not directly correlated with wind speed or relative humidity separately. However, selecting the data for high wind speeds (>4m/s at 10 m elevation), a definite trend is observed between dust concentration and relative humidity: dust concentration increases with relative humidity, reaching a maximum around 25% and it subsequently decreases with relative humidity. Models for dust storm forecasting may be improved by utilizing atmospheric humidity and wind speed as main drivers for dust generation and transport. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. Hurricane Imaging Radiometer Wind Speed and Rain Rate Retrievals during the 2010 GRIP Flight Experiment

    Science.gov (United States)

    Sahawneh, Saleem; Farrar, Spencer; Johnson, James; Jones, W. Linwood; Roberts, Jason; Biswas, Sayak; Cecil, Daniel

    2014-01-01

    Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive microwave airborne remote sensor. The Hurricane Imaging Radiometer, HIRAD, will extend the nadir viewing SFMR capability to provide wide swath images of wind speed and rain rate, while flying on a high altitude aircraft. HIRAD was first flown in the Genesis and Rapid Intensification Processes, GRIP, NASA hurricane field experiment in 2010. This paper reports on geophysical retrieval results and provides hurricane images from GRIP flights. An overview of the HIRAD instrument and the radiative transfer theory based, wind speed/rain rate retrieval algorithm is included. Results are presented for hurricane wind speed and rain rate for Earl and Karl, with comparison to collocated SFMR retrievals and WP3D Fuselage Radar images for validation purposes.

  4. Optimal wind energy penetration in power systems: An approach based on spatial distribution of wind speed

    International Nuclear Information System (INIS)

    Zolfaghari, Saeed; Riahy, Gholam H.; Abedi, Mehrdad; Golshannavaz, Sajjad

    2016-01-01

    Highlights: • Chronological wind speeds at distinct locations of the wind farm are not the same. • Spatial distribution of wind speed affects wind farm’s output power expectation. • Neglecting wind speed’s spatial doubt leads to mistake in wind energy penetration. • Scenario-based method can be used for effective wind capacity penetration level. - Abstract: Contributing in power system expansions, the present study establishes an efficient scheme for optimal integration of wind energy resources. The proposed approach highly concerns the spatial distribution of wind speed at different points of a wind farm. In mathematical statements, a suitable probability distribution function (PDF) is well-designed for representing such uncertainties. In such conditions, it is likely to have dissimilar output powers for individual and identical wind turbines. Thus, the overall aggregated PDF of a wind farm remarkably influences the critical parameters including the expected power and energy, capacity factor, and the reliability metrics such as loss of load expectation (LOLE) and expected energy not supplied (EENS). Furthermore, the proposed approach is deployed for optimal allocation of wind energy in bulk power systems. Hence, two typical test systems are numerically analyzed to interrogate the performance of the proposed approach. The conducted survey discloses an over/underestimation of harvestable wind energy in the case of overlooking spatial distributions. Thus, inaccurate amounts of wind farm’s capacity factor, output power, energy and reliability indices might be estimated. Meanwhile, the number of wind turbines may be misjudged to be installed. However, the proposed approach yields in a fair judgment regarding the overall performance of the wind farm. Consequently, a reliable penetration level of wind energy to the power system is assured. Extra discussions are provided to deeply assess the promising merits of the founded approach.

  5. Combined time-varying forecast based on the proper scoring approach for wind power generation

    DEFF Research Database (Denmark)

    Chen, Xingying; Jiang, Yu; Yu, Kun

    2017-01-01

    Compared with traditional point forecasts, combined forecast have been proposed as an effective method to provide more accurate forecasts than individual model. However, the literature and research focus on wind-power combined forecasts are relatively limited. Here, based on forecasting error...... distribution, a proper scoring approach is applied to combine plausible models to form an overall time-varying model for the next day forecasts, rather than weights-based combination. To validate the effectiveness of the proposed method, real data of 3 years were used for testing. Simulation results...... demonstrate that the proposed method improves the accuracy of overall forecasts, even compared with a numerical weather prediction....

  6. Multifractal and local correlation of simultaneous wind speed-power output from a single wind trubine

    Science.gov (United States)

    Calif, Rudy; Schmitt, François G.; Huang, Yongxiang

    2014-05-01

    The wind energy production is a nonlinear and no stationary resource, due to the intermittent statistics of atmospheric wind speed at all spatial and temporal scales ranging from large scale variations to very short scale variations. Recently, Rudy et al.[1] observed the intermittent and multifractal properties of wind energy production. Classically, IEC standard 4100 is used by the wind energy community, for modeling the interactions of wind speed with the wind turbine. However, this model reflects gaussian statistics contrary to observed wind and energy production measurements. Modeling of power curve of a single wind turbine remains a challenge. The precise understanding of the dynamics of nonlinear power curve over very short time scales, is necessary. Hence, multifractal cross-correlation methods such as Generalized Correlations Exponents (GCE), multifractal detrended cross-correlation analysis (MFXDFA), multifractal detrending moving average cross-correlation analysis (MFXDMA) are applied to simultaneous wind speed power output from a single wind turbine to determine the nature of scaling correlation behavior. Furthermore, in order to detect eventual local correlation, an application of empirical mode decomposition based on time dependent intrinsic correlation to simultaneous measurements is performed. The simultaneous wind speed-power output measurements are recorded continuously with a sampling rate f = 1Hz, during 115 days in 2006. The wind speed measurements are obtained at 31 m above the ground, and the power output is delivered by 500 kW Nordtank wind turbine positionned at the Technical University, Risœ, Denmark. References [1] Calif, R., Schmitt, F.G., Huang, Y., Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis, Physica, 392, 4106-4120, 2013.

  7. Reactive power control methods for improved reliability of wind power inverters under wind speed variations

    DEFF Research Database (Denmark)

    Ma, Ke; Liserre, Marco; Blaabjerg, Frede

    2012-01-01

    temperature fluctuation in the most stressed devices of 3L-NPC wind power inverter under severe wind speed variations can be significantly stabilized, and the reliability of the power converter can thereby be improved while the increased stress of the other devices in the same power converter......The thermal cycling of power switching devices may lead to failures that compromise the reliability of power converters. Wind Turbine Systems (WTS) are especially subject to severe thermal cycling which may be caused by the wind speed variations or power grid faults. This paper proposes a control...... method to relieve the thermal cycling of power switching devices under severe wind speed variations, by circulating reactive power among the parallel power converters in a WTS or among the WTS's in a wind park. The amount of reactive power is adjusted to limit the junction temperature fluctuation...

  8. Dynamic behavior of parked wind turbine at extreme wind speed

    DEFF Research Database (Denmark)

    Totsuka, Yoshitaka; Imamura, Hiroshi; Yde, Anders

    2016-01-01

    In wind turbine design process, a series of load analysis is generally performed to determine ultimate and fatigue loads under various design load cases (DLCs) which is specified in IEC 61400. These design load scenario covers not only normal operating condition but also startup, shutdown, parked...... of standstill and idling is analyzed by time domain simulations using two different coupled aero-hydro-servo-elastic codes. Trend in modern wind turbines is development of bigger, lighter and more flexible rotors where vibration issues may cause aero-elastic instabilities which have a serious impact...

  9. Spatio-temporal modelling for short term wind power forecasts. Why, when and how.

    Science.gov (United States)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2017-04-01

    This study is based on a case study of 349 wind farms in Western Denmark with available energy production every 15 minutes for 6 years. Our aim is to do short term forecasting up to 5 hours ahead based on previous observations. We want sharp and calibrated probabilistic forecasts for both individual wind farms and for aggregated energy production, for example the energy production in the whole region. To obtain this we propose two Bayesian spatio-temporal models, and obtain full probabilistic forecasts of wind power. The models are based on the stochastic partial differential equation (SPDE) approach to spatial-temporal modelling which enables fast inference using integrated nested Laplace approximations (INLA) as well as dimension reduction. We provide detailed analysis on the forecast performances on the individual and aggregated level based on appropriate metrics tailored for probability forecasts for both the spatial temporal models as well as for temporal models for individual wind farms. The case study as well as simulation studies demonstrate that forecasts that are individually reliable do not need to produce an aggregated forecasts that are reliable. Indeed, the case study shows that even when all individual forecasts are calibrated can the aggregated forecasts be so uncalibrated that less that 20% of the observations fall within the 95% forecast interval. T he results and methodology are both relevant for wind power forecasts in other regions as well as for spatial-temporal modeling and decisions in general.

  10. Flicker Mitigation by Speed Control of Permanent Magnet Synchronous Generator Variable-Speed Wind Turbines

    DEFF Research Database (Denmark)

    Hu, Weihao; Zhang, Yunqian; Chen, Zhe

    2013-01-01

    Grid-connected wind turbines are fluctuating power sources that may produce flicker during continuous operation. This paper presents a simulation model of a MW-level variable speed wind turbine with a full-scale back-to-back power converter and permanent magnet synchronous generator (PMSG...

  11. High-speed solar wind flow parameters at 1 AU

    International Nuclear Information System (INIS)

    Feldman, W.C.; Asbridge, J.R.; Bame, S.J.; Gosling, J.T.

    1976-01-01

    To develop a set of constraints for theories of solar wind high-speed streams, a detailed study was made of the fastest streams observed at 1 AU during the time period spanning March 1971 through July 1974. Streams were accepted for study only if (1) the maximum speed exceeded 650 km s -1 ; (2) effects of stream-stream dynamical interaction on the flow parameters could be safely separated from the intrinsic characteristics of the high-speed regions; (3) the full width at half maximum (FWHM) of the stream when mapped back to 20 solar radii by using a constant speed approximation was greater than 45degree in Carrington longitude; and (4) there were no obvious solar-activity-induced contaminating effects. Nineteen streams during this time interval satisfied these criteria. Average parameters at 1 AU for those portions of these streams above V=650 km s -1 are given.Not only is it not presently known why electrons are significantly cooler than the protons within high-speed regions, but also observed particle fluxes and convected energy fluxes for speed greater than 650 km s -1 are substantially larger than those values predicted by any of the existing theories of solar wind high-speed streams. More work is therefore needed in refining present solar wind models to see whether suitable modifications and/or combinations of existing theories based on reasonable coronal conditions can accommodate the above high-speed flow parameters

  12. Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

    DEFF Research Database (Denmark)

    Iversen, Jan Emil Banning; Morales González, Juan Miguel; Møller, Jan Kloppenborg

    2017-01-01

    Short-term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per-horizon-basis, meaning that they lack information about the development of the u...

  13. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

    KAUST Repository

    Elkantassi, Soumaya

    2017-04-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  14. Survey of variable speed operation of wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Carlson, Ola; Hylander, J.; Thorborg, K. [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Electric Power Engineering

    1996-12-01

    During the last five years the production and operation of variable-speed wind turbines have advanced from a few experimental machines to a serial production of at least 10 MW of installed capacity of variable speed machines per week. The rated power of serial wind turbines is today around 600 kW and for the prototypes up to 3000 kW. Variable speed operation of wind turbines can be obtained with several different types of electrical generating systems, such as synchronous generators with diode rectifiers and thyristor inverters or induction generators with IGBT-converters, for the wide speed range. For the narrow speed range the wound motor induction generator with a rotor cascade or a controlled rotor resistance is preferable. The development of permanent magnetic material and the reduction of costs of the power electronic components have opened a possibility of designing cost-effective wind turbines with a directly driven generator. Pitch control together with variable speed will make it possible to limit the power variation within a few percent, 2 to 5 %, of the rated power. 7 refs, 4 figs, 2 tabs

  15. Swatch Testing at Elevated Wind Speeds

    Science.gov (United States)

    2014-07-17

    Systems, Co.) used previously but was now placed in a pressure vessel. The atomized spray was sent through a dryer to a dissemination array placed in the...the wind tunnel. The air-cap spray head ejected Aerosil into a capped pipe that radially ejected the particles into the box. Downstream of the...also might be used in such a system. As a prototype, a paint spray nozzle (W.R. Brown Co., North Chicago, Illinois, Model Speedy) and associated

  16. ARE660 Wind Generator: Low Wind Speed Technology for Small Turbine Development

    Energy Technology Data Exchange (ETDEWEB)

    Robert W. Preus; DOE Project Officer - Keith Bennett

    2008-04-23

    This project is for the design of a wind turbine that can generate most or all of the net energy required for homes and small businesses in moderately windy areas. The purpose is to expand the current market for residential wind generators by providing cost effective power in a lower wind regime than current technology has made available, as well as reduce noise and improve reliability and safety. Robert W. Preus’ experience designing and/or maintaining residential wind generators of many configurations helped identify the need for an improved experience of safety for the consumer. Current small wind products have unreliable or no method of stopping the wind generator in fault or high wind conditions. Consumers and their neighbors do not want to hear their wind generators. In addition, with current technology, only sites with unusually high wind speeds provide payback times that are acceptable for the on-grid user. Abundant Renewable Energy’s (ARE) basic original concept for the ARE660 was a combination of a stall controlled variable speed small wind generator and automatic fail safe furling for shutdown. The stall control for a small wind generator is not novel, but has not been developed for a variable speed application with a permanent magnet alternator (PMA). The fail safe furling approach for shutdown has not been used to our knowledge.

  17. Alternative methods of estimating hub-height wind speed for small wind turbine performance evaluation

    Science.gov (United States)

    Ziter, Brett

    Current industry standards for evaluating wind turbine power performance require erecting a meteorological mast on site to obtain reference measurements of hub-height wind speed. New considerations for small wind turbines (SWTs) offer the alternative of using an anemometer extending from a lower elevation on the turbine tower. In either case, SWT owners face questions and impracticalities when applying this standard in-situ. Alternative methods of predicting hub-height wind speed for SWT performance evaluation have been assessed experimentally using a Bergey XL.1 SWT collocated with a meteorological mast. Findings indicate that vertical extrapolation can increase the accuracy of tower-mounted anemometry for predicting hub-height wind speed. It is recommended to use concurrent wind speed measurements from anemometers at two elevations to develop site-specific wind shear parameters. Three-dimensional wind speed data from a sonic anemometer were used alongside a theoretical model to determine the optimal location for the topmost anemometer but results were inconclusive.

  18. WIND TURBINE OPERATION PARAMETER CHARACTERISTICS AT A GIVEN WIND SPEED

    Directory of Open Access Journals (Sweden)

    Zdzisław Kamiński

    2014-06-01

    Full Text Available This paper discusses the results of the CFD simulation of the flow around Vertical Axis Wind Turbine rotor. The examined rotor was designed following patent application no. 402214. The turbine operation is characterised by parameters, such as opening angle of blades, power, torque, rotational velocity at a given wind velocity. Those parameters have an impact on the performance of entire assembly. The distribution of forces acting on the working surfaces in the turbine can change, depending on the angle of rotor rotation. Moreover, the resultant force derived from the force acting on the oncoming and leaving blades should be as high as possible. Accordingly, those parameters were individually simulated over time for each blade in three complete rotations. The attempts to improve the performance of the entire system resulted in a new research trend to improve the performance of working turbine rotor blades.

  19. Wind Speed Preview Measurement and Estimation for Feedforward Control of Wind Turbines

    Science.gov (United States)

    Simley, Eric J.

    Wind turbines typically rely on feedback controllers to maximize power capture in below-rated conditions and regulate rotor speed during above-rated operation. However, measurements of the approaching wind provided by Light Detection and Ranging (lidar) can be used as part of a preview-based, or feedforward, control system in order to improve rotor speed regulation and reduce structural loads. But the effectiveness of preview-based control depends on how accurately lidar can measure the wind that will interact with the turbine. In this thesis, lidar measurement error is determined using a statistical frequency-domain wind field model including wind evolution, or the change in turbulent wind speeds between the time they are measured and when they reach the turbine. Parameters of the National Renewable Energy Laboratory (NREL) 5-MW reference turbine model are used to determine measurement error for a hub-mounted circularly-scanning lidar scenario, based on commercially-available technology, designed to estimate rotor effective uniform and shear wind speed components. By combining the wind field model, lidar model, and turbine parameters, the optimal lidar scan radius and preview distance that yield the minimum mean square measurement error, as well as the resulting minimum achievable error, are found for a variety of wind conditions. With optimized scan scenarios, it is found that relatively low measurement error can be achieved, but the attainable measurement error largely depends on the wind conditions. In addition, the impact of the induction zone, the region upstream of the turbine where the approaching wind speeds are reduced, as well as turbine yaw error on measurement quality is analyzed. In order to minimize the mean square measurement error, an optimal measurement prefilter is employed, which depends on statistics of the correlation between the preview measurements and the wind that interacts with the turbine. However, because the wind speeds encountered by

  20. A New Fault Diagnosis Algorithm for PMSG Wind Turbine Power Converters under Variable Wind Speed Conditions

    Directory of Open Access Journals (Sweden)

    Yingning Qiu

    2016-07-01

    Full Text Available Although Permanent Magnet Synchronous Generator (PMSG wind turbines (WTs mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and current vector trajectory pattern approaches. The results show that the proposed method has advantages in the accuracy of fault diagnosis and has superior anti-noise capability in variable wind speed situations. The impact of the control strategy is also identified. Experimental results demonstrate its applicability on practical WT condition monitoring system which is used to improve wind turbine reliability and reduce their maintenance cost.

  1. Reduced wind speed improves plant growth in a desert city.

    Directory of Open Access Journals (Sweden)

    Christofer Bang

    2010-06-01

    Full Text Available The often dramatic effects of urbanization on community and ecosystem properties, such as primary productivity, abundances, and diversity are now well-established. In most cities local primary productivity increases and this extra energy flows upwards to alter diversity and relative abundances in higher trophic levels. The abiotic mechanisms thought to be responsible for increases in urban productivity are altered temperatures and light regimes, and increased nutrient and water inputs. However, another abiotic factor, wind speed, is also influenced by urbanization and well known for altering primary productivity in agricultural systems. Wind effects on primary productivity have heretofore not been studied in the context of urbanization.We designed a field experiment to test if increased plant growth often observed in cities is explained by the sheltering effects of built structures. Wind speed was reduced by protecting Encelia farinosa (brittlebush plants in urban, desert remnant and outlying desert localities via windbreaks while controlling for water availability and nutrient content. In all three habitats, we compared E. farinosa growth when protected by experimental windbreaks and in the open. E. farinosa plants protected against ambient wind in the desert and remnant areas grew faster in terms of biomass and height than exposed plants. As predicted, sheltered plants did not differ from unprotected plants in urban areas where wind speed is already reduced.Our results indicate that reductions in wind speed due to built structures in cities contribute to increased plant productivity and thus also to changes in abundances and diversity of higher trophic levels. Our study emphasizes the need to incorporate wind speed in future urban ecological studies, as well as in planning for green space and sustainable cities.

  2. Reduced wind speed improves plant growth in a desert city.

    Science.gov (United States)

    Bang, Christofer; Sabo, John L; Faeth, Stanley H

    2010-06-10

    The often dramatic effects of urbanization on community and ecosystem properties, such as primary productivity, abundances, and diversity are now well-established. In most cities local primary productivity increases and this extra energy flows upwards to alter diversity and relative abundances in higher trophic levels. The abiotic mechanisms thought to be responsible for increases in urban productivity are altered temperatures and light regimes, and increased nutrient and water inputs. However, another abiotic factor, wind speed, is also influenced by urbanization and well known for altering primary productivity in agricultural systems. Wind effects on primary productivity have heretofore not been studied in the context of urbanization. We designed a field experiment to test if increased plant growth often observed in cities is explained by the sheltering effects of built structures. Wind speed was reduced by protecting Encelia farinosa (brittlebush) plants in urban, desert remnant and outlying desert localities via windbreaks while controlling for water availability and nutrient content. In all three habitats, we compared E. farinosa growth when protected by experimental windbreaks and in the open. E. farinosa plants protected against ambient wind in the desert and remnant areas grew faster in terms of biomass and height than exposed plants. As predicted, sheltered plants did not differ from unprotected plants in urban areas where wind speed is already reduced. Our results indicate that reductions in wind speed due to built structures in cities contribute to increased plant productivity and thus also to changes in abundances and diversity of higher trophic levels. Our study emphasizes the need to incorporate wind speed in future urban ecological studies, as well as in planning for green space and sustainable cities.

  3. Tip Speed Ratio Based Maximum Power Tracking Control of Variable Speed Wind Turbines; A Comprehensive Design

    Directory of Open Access Journals (Sweden)

    Murat Karabacak

    2017-08-01

    Full Text Available The most primitive control method of wind turbines used to generate electric energy from wind is the fixed speed control method. With this method, it is not possible that turbine input power is transferred to grid at maximum rate. For this reason, Maximum Power Tracking (MPT schemes are proposed. In order to implement MPT, the propeller has to rotate at a different speed for every different wind speed. This situation has led MPT based systems to be called Variable Speed Wind Turbine (VSWT systems. In VSWT systems, turbine input power can be transferred to grid at rates close to maximum power. When MPT based control of VSWT systems is the case, two important processes come into prominence. These are instantaneously determination and tracking of MPT point. In this study, using a Maximum Power Point Tracking (MPPT method based on tip speed ratio, power available in wind is transferred into grid over a back to back converter at maximum rate via a VSWT system with permanent magnet synchronous generator (PMSG. Besides a physical wind turbine simulator is modelled and simulated. Results show that a time varying MPPT point is tracked with a high performance.

  4. Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [Univ. of Texas-Dallas, Richardson, TX (United States); Feng, Cong [Univ. of Texas-Dallas, Richardson, TX (United States); Wang, Zhenke [Univ. of Texas-Dallas, Richardson, TX (United States); Zhang, Jie [Univ. of Texas-Dallas, Richardson, TX (United States)

    2017-08-31

    Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

  5. Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [University of Texas at Dallas; Feng, Cong [University of Texas at Dallas; Wang, Zhenke [University of Texas at Dallas; Zhang, Jie [University of Texas at Dallas

    2018-02-01

    Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

  6. Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

    International Nuclear Information System (INIS)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

    2014-01-01

    Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

  7. Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

    Directory of Open Access Journals (Sweden)

    Erasmo Cadenas

    2016-02-01

    Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

  8. Medium-range fire weather forecasts

    Science.gov (United States)

    J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

    1991-01-01

    The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

  9. Model of a synthetic wind speed time series generator

    DEFF Research Database (Denmark)

    Negra, N.B.; Holmstrøm, O.; Bak-Jensen, B.

    2008-01-01

    is described and some statistical issues (seasonal characteristics, autocorrelation functions, average values and distribution functions) are used for verification. The output of the model has been designed as input for sequential Monte Carlo simulation; however, it is expected that it can be used for other...... of the main elements to consider for this purpose is the model of the wind speed that is usually required as input. Wind speed measurements may represent a solution for this problem, but, for techniques such as sequential Monte Carlo simulation, they have to be long enough in order to describe a wide range...

  10. Wind speed and direction shears with associated vertical motion during strong surface winds

    Science.gov (United States)

    Alexander, M. B.; Camp, D. W.

    1984-01-01

    Strong surface winds recorded at the NASA 150-Meter Ground Winds Tower facility at Kennedy Space Center, Florida, are analyzed to present occurrences representative of wind shear and vertical motion known to be hazardous to the ascent and descent of conventional aircraft and the Space Shuttle. Graphical (percentage frequency distributions) and mathematical (maximum, mean, standard deviation) descriptions of wind speed and direction shears and associated updrafts and downdrafts are included as functions of six vertical layers and one horizontal distance for twenty 5-second intervals of parameters sampled simultaneously at the rate of ten per second during a period of high surface winds.

  11. Increase of the Integration Degree of Wind Power Plants into the Energy System Using Wind Forecasting and Power Consumption Predictor Models by Transmission System Operator

    Directory of Open Access Journals (Sweden)

    Manusov V.Z.

    2017-12-01

    Full Text Available Wind power plants’ (WPPs high penetration into the power system leads to various inconveniences in the work of system operators. This fact is associated with the unpredictable nature of wind speed and generated power, respectively. Due to these factors, such source of electricity must be connected to the power system to avoid detrimental effects on the stability and quality of electricity. The power generated by the WPPs is not regulated by the system operator. Accurate forecasting of wind speed and power, as well as power load can solve this problem, thereby making a significant contribution to improving the power supply systems reliability. The article presents a mathematical model for the wind speed prediction, which is based on autoregression and fuzzy logic derivation of Takagi-Sugeno. The new model of wavelet transform has been developed, which makes it possible to include unnecessary noise from the model, as well as to reveal the cycling of the processes and their trend. It has been proved, that the proposed combination of methods can be used simultaneously to predict the power consumption and the wind power plant potential power at any time interval, depending on the planning horizon. The proposed models support a new scientific concept for the predictive control system of wind power stations and increase their degree integration into the electric power system.

  12. An integrated system for wind energy forecast using meteorological models and statistical post-processing

    Science.gov (United States)

    Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.

    2009-12-01

    With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric system implies a strong requirement for accurate wind forecasts, that can be used to forecast this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a system for 72h energy forecast in mainland Portugal was setup, using 6km resolution meteorological forecasts, forced by global GFS forecasts by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed system, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological forecast, and converts the forecasted wind fields into forecasted energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind forecasts, requiring fully developed meteorological models. In general, results from 2 full years of

  13. Reliability of Wind Speed Data from Satellite Altimeter to Support Wind Turbine Energy

    Science.gov (United States)

    Uti, M. N.; Din, A. H. M.; Omar, A. H.

    2017-10-01

    Satellite altimeter has proven itself to be one of the important tool to provide good quality information in oceanographic study. Nowadays, most countries in the world have begun in implementation the wind energy as one of their renewable energy for electric power generation. Many wind speed studies conducted in Malaysia using conventional method and scientific technique such as anemometer and volunteer observing ships (VOS) in order to obtain the wind speed data to support the development of renewable energy. However, there are some limitations regarding to this conventional method such as less coverage for both spatial and temporal and less continuity in data sharing by VOS members. Thus, the aim of this research is to determine the reliability of wind speed data by using multi-mission satellite altimeter to support wind energy potential in Malaysia seas. Therefore, the wind speed data are derived from nine types of satellite altimeter starting from year 1993 until 2016. Then, to validate the reliability of wind speed data from satellite altimeter, a comparison of wind speed data form ground-truth buoy that located at Sabah and Sarawak is conducted. The validation is carried out in terms of the correlation, the root mean square error (RMSE) calculation and satellite track analysis. As a result, both techniques showing a good correlation with value positive 0.7976 and 0.6148 for point located at Sabah and Sarawak Sea, respectively. It can be concluded that a step towards the reliability of wind speed data by using multi-mission satellite altimeter can be achieved to support renewable energy.

  14. Improving Wind Ramp Forecasts by the HRRR System via Statistical Postprocessing

    Science.gov (United States)

    Worsnop, Rochelle; Scheuerer, Michael; Hamill, Thomas M.

    2017-04-01

    Wind power forecasting is gaining enormous international significance as more and more countries and regions enact policies to increase the use of renewable energy. Wind ramps pose a particular challenge in decision-making processes in the wind energy industry since a sudden decrease or increase in wind energy production must be balanced by conventional power generators and could be costly for wind farm operators. In this study, we assess the performance of the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model in predicting wind ramps with up to 12 hours of lead time at two wind tower locations in the United States. Novel statistical postprocessing methodology is used to generate scenarios of short-term wind power production; this probabilistic enhancement of the deterministic HRRR forecasts significantly improves the skill in predicting wind ramp events, and could be implemented by wind farm operators to support decision-making.

  15. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, J.; Hodge, B. M.; Florita, A.

    2013-05-01

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  16. Comparison of the economic impact of different wind power forecast systems for producers

    Science.gov (United States)

    Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

    2014-05-01

    Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a

  17. On the wind speed reduction in the center of large clusters of wind turbines

    DEFF Research Database (Denmark)

    Frandsen, Sten Tronæs

    1992-01-01

    of the wind speed assuming the wind turbines effectively act as roughness elements. The model makes use of similarities to so-called canopy flows, where the surface drag and the drag on individual obstacles are added to form the total drag. Results are compared with existing models for reduction of efficiency...

  18. Wind tunnel experiments to prove a hydraulic passive torque control concept for variable speed wind turbines

    NARCIS (Netherlands)

    Diepeveen, N.F.B.; Jarquin-Laguna, A.

    2014-01-01

    In this paper the results are presented of experiments to prove an innovative concept for passive torque control of variable speed wind turbines using fluid power technology. It is demonstrated that by correctly configuring the hydraulic drive train, the wind turbine rotor operates at or near

  19. design of a small scale wind generator for low wind speed areas

    African Journals Online (AJOL)

    USER

    the complexity of the drive train there are experimental proposals in literature where a synchronous generator that be able to operate under low wind speed can be directly connected to the end user especially the off-grid population. Hence, the study designed a six pole pairs wind turbine generator using permanent magnet ...

  20. On Using Wind Speed Preview to Reduce Wind Turbine Tower Oscillations

    DEFF Research Database (Denmark)

    Kristalny, Maxim; Madjidian, Daria; Knudsen, Torben

    2013-01-01

    We investigate the potential of using previewed wind speed measurements for damping wind turbine fore-aft tower oscillations. Using recent results on continuous-time H 2 preview control, we develop a numerically efficient framework for the feedforward controller synthesis. One of the major benefits...

  1. The large scale and long term evolution of the solar wind speed distribution and high speed streams

    International Nuclear Information System (INIS)

    Intriligator, D.S.

    1977-01-01

    The spatial and temporal evolution of the solar wind speed distribution and of high speed streams in the solar wind are examined. Comparisons of the solar wind streaming speeds measured at Earth, Pioneer 11, and Pioneer 10 indicate that between 1 AU and 6.4 AU the solar wind speed distributions are narrower (i.e. the 95% value minus the 5% value of the solar wind streaming speed is less) at extended heliocentric distances. These observations are consistent with one exchange of momentum in the solar wind between high speed streams and low speed streams as they propagate outward from the Sun. Analyses of solar wind observations at 1 AU from mid 1964 through 1973 confirm the earlier results reported by Intriligator (1974) that there are statistically significant variations in the solar wind in 1968 and 1969, years of solar maximum. High speed stream parameters show that the number of high speed streams in the solar wind in 1968 and 1969 is considerably more than the predicted yearly average, and in 1965 and 1972 less. Histograms of solar wind speed from 1964 through 1973 indicate that in 1968 there was the highest percentage of elevated solar wind speeds and in 1965 and 1972 the lowest. Studies by others also confirm these results although the respective authors did not indicate this fact. The duration of the streams and the histograms for 1973 imply a shifting in the primary stream source. (Auth.)

  2. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  3. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional “Upwind” Scheme

    Science.gov (United States)

    Riley, Pete

    2017-01-01

    Abstract Long lead‐time space‐weather forecasting requires accurate prediction of the near‐Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near‐Sun solar wind and magnetic field conditions provide the inner boundary condition to three‐dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics‐based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near‐Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near‐Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near‐Sun solar wind speed at a range of latitudes about the sub‐Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun‐Earth line. Propagating these conditions to Earth by a three‐dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one‐dimensional “upwind” scheme is used. The variance in the resulting near‐Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996–2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large). PMID:29398982

  4. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    2014-01-01

    energy. The present study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by downscaling global numerical weather prediction (NWP) data from the National Centers for Environmental Prediction Global Forecast System (GFS) using the limited-area NWP model described...

  5. Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2016-01-01

    Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

  6. A Comparison of Wind Speed Data from Mechanical and Ultrasonic Anemometers

    Science.gov (United States)

    Short, D.; Wells, L.; Merceret, F.; Roeder, W. P.

    2006-01-01

    This study compared the performance of mechanical and ultrasonic anemometers at the Eastern Range (ER; Kennedy Space Center and Cape Canaveral Air Force Station on Florida's Atlantic coast) and the Western Range (WR; Vandenberg Air Force Base on California's Pacific coast). Launch Weather Officers, forecasters, and Range Safety analysts need to understand the performance of wind sensors at the ER and WR for weather warnings, watches, advisories, special ground processing operations, launch pad exposure forecasts, user Launch Commit Criteria (LCC) forecasts and evaluations, and toxic dispersion support. The current ER and WR weather tower wind instruments are being changed from the current propeller-and-vane (ER) and cup-and-vane (WR) sensors to ultrasonic sensors through the Range Standardization and Automation (RSA) program. The differences between mechanical and ultrasonic techniques have been found to cause differences in the statistics of peak wind speed in previous studies. The 45th Weather Squadron (45 WS) and the 30th Weather Squadron (30 WS) requested the Applied Meteorology Unit (AMU) to compare data between RSA and current sensors to determine if there are significant differences. Approximately 3 weeks of Legacy and RSA wind data from each range were used in the study, archived during May and June 2005. The ER data spanned the full diurnal cycle, while the WR data was confined to 1000-1600 local time. The sample of 1-minute data from numerous levels on 5 different towers on each range totaled more than 500,000 minutes of data (482,979 minutes of data after quality control). The 10 towers were instrumented at several levels, ranging from 12 ft to 492 ft above ground level. The RSA sensors were collocated at the same vertical levels as the present sensors and typically within 15 ft horizontally of each another. Data from a total of 53 RSA ultrasonic sensors, collocated with present sensors were compared. The 1-minute average wind speed/direction and the 1

  7. Increased Surface Wind Speeds Follow Diminishing Arctic Sea Ice

    Science.gov (United States)

    Mioduszewski, J.; Vavrus, S. J.; Wang, M.; Holland, M. M.; Landrum, L.

    2017-12-01

    Projections of Arctic sea ice through the end of the 21st century indicate the likelihood of a strong reduction in ice area and thickness in all seasons, leading to a substantial thermodynamic influence on the overlying atmosphere. This is likely to have an effect on winds over the Arctic Basin, due to changes in atmospheric stability and/or baroclinicity. Prior research on future Arctic wind changes is limited and has focused mainly on the practical impacts on wave heights in certain seasons. Here we attempt to identify patterns and likely mechanisms responsible for surface wind changes in all seasons across the Arctic, particularly those associated with sea ice loss in the marginal ice zone. Sea level pressure, near-surface (10 m) and upper-air (850 hPa) wind speeds, and lower-level dynamic and thermodynamic variables from the Community Earth System Model Large Ensemble Project (CESM-LE) were analyzed for the periods 1971-2000 and 2071-2100 to facilitate comparison between a present-day and future climate. Mean near-surface wind speeds over the Arctic Ocean are projected to increase by late century in all seasons but especially during autumn and winter, when they strengthen by up to 50% locally. The most extreme wind speeds in the 90th percentile change even more, increasing in frequency by over 100%. The strengthened winds are closely linked to decreasing lower-tropospheric stability resulting from the loss of sea ice cover and consequent surface warming (locally over 20 ºC warmer in autumn and winter). A muted pattern of these future changes is simulated in CESM-LE historical runs from 1920-2005. The enhanced winds near the surface are mostly collocated with weaker winds above the boundary layer during autumn and winter, implying more vigorous vertical mixing and a drawdown of high-momentum air.The implications of stronger future winds include increased coastal hazards and the potential for a positive feedback with sea ice by generating higher winds and

  8. Variations in long term wind speed during different decades in ...

    Indian Academy of Sciences (India)

    A study has been carried out by comparing the extreme wind speeds estimated based on. NCEP/NCAR reanalysis data for 100 years return period using Fischer Tippet-1 (commonly known as Gumbel) and Weibull distributions for three locations (off Goa, Visakhapatnam and Machili- patnam) in the north Indian Ocean.

  9. Variations in long term wind speed during different decades in ...

    Indian Academy of Sciences (India)

    A study has been carried out by comparing the extreme wind speeds estimated based on NCEP/NCAR reanalysis data for 100 years return period using Fischer Tippet-1 (commonly known as Gumbel)and Weibull distributions for three locations (off Goa,Visakhapatnam and Machilipatnam)in the north Indian Ocean.

  10. LQG Controller Design for Pitch Regulated Variable Speed Wind Turbine

    DEFF Research Database (Denmark)

    Imran, Raja Muhammed; Hussain, Dil Muhammad Akbar; Chen, Zhe

    2014-01-01

    Variable speed wind turbine is a complex and nonlinear system, a sophisticated control is required to meet the challenges posed by these systems. This paper is presenting a pitch regulation strategy based on LQG (Linear Quadratic Gaussian) to regulate turbine at its rated power and to reject...

  11. Variable speed control for Vertical Axis Wind Turbine

    DEFF Research Database (Denmark)

    Galinos, Christos; Larsen, Torben J.

    the Inflow project. The investigation of the VAWT performance under different control parameters such as the PI gains has been performed by Christos Galinos. Deterministic and turbulent wind speed steps of 2 m/s from 6 m/s to 24 m/s and back to 12 m/s are applied. The controller gives smooth transient...

  12. Variable Speed Wind Turbines Capability for Temporary Over-Production

    DEFF Research Database (Denmark)

    Tarnowski, Germán Claudio; Kjær, Philip Carne; Sørensen, Poul Ejnar

    2009-01-01

    New control systems for Variable Speed Wind Turbines (VSWT) need to be developed in order to provide inertia response and frequency control to support the grid. This work studies the behavior and capability of VSWT for providing temporary active power overproduction. The study is conducted...

  13. Gain-scheduled Linear Quadratic Control of Wind Turbines Operating at High Wind Speed

    DEFF Research Database (Denmark)

    Østergaard, Kasper Zinck; Stoustrup, Jakob; Brath, Per

    2007-01-01

    This paper addresses state estimation and linear quadratic (LQ) control of variable speed variable pitch wind turbines. On the basis of a nonlinear model of a wind turbine, a set of operating conditions is identified and a LQ controller is designed for each operating point. The controller gains...... are then interpolated linearly to get a control law for the entire operating envelope. A nonlinear state estimator is designed as a combination of two unscented Kalman filters and a linear disturbance estimator. The gain-scheduling variable (wind speed) is then calculated from the output of these state estimators...

  14. Weather Research and Forecasting model simulation of an onshore wind farm: assessment against LiDAR and SCADA data

    Science.gov (United States)

    Santoni, Christian; Garcia-Cartagena, Edgardo J.; Zhan, Lu; Iungo, Giacomo Valerio; Leonardi, Stefano

    2017-11-01

    The integration of wind farm parameterizations into numerical weather prediction models is essential to study power production under realistic conditions. Nevertheless, recent models are unable to capture turbine wake interactions and, consequently, the mean kinetic energy entrainment, which are essential for the development of power optimization models. To address the study of wind turbine wake interaction, one-way nested mesoscale to large-eddy simulation (LES) were performed using the Weather Research and Forecasting model (WRF). The simulation contains five nested domains modeling the mesoscale wind on the entire North Texas Panhandle region to the microscale wind fluctuations and turbine wakes of a wind farm located at Panhandle, Texas. The wind speed, direction and boundary layer profile obtained from WRF were compared against measurements obtained with a sonic anemometer and light detection and ranging system located within the wind farm. Additionally, the power production were assessed against measurements obtained from the supervisory control and data acquisition system located in each turbine. Furthermore, to incorporate the turbines into very coarse LES, a modification to the implementation of the wind farm parameterization by Fitch et al. (2012) is proposed. This work was supported by the NSF, Grants No. 1243482 (WINDINSPIRE) and IIP 1362033 (WindSTAR), and TACC.

  15. Wind turbine blades for harnessing energy from Malaysian low speed wind - manufacturing technique

    International Nuclear Information System (INIS)

    Abas Abd Wahab; Azmin Shakrine

    2000-01-01

    Blades for wind turbine to harness energy in the Malaysia low speed winds have been designed. During wind tunnel testing, wind turbine model using this type of blades has cut in speed of 1.5 m/s and turned at 450 rpm at 4 m/s wind. The blades, due to their critical dimensions of 1.2 m length, 5 cm thickness, tapered and 15 degree twist, were difficult to produce especially in large number. Several production methods have been studied but for economical mass production, fibreglass blades using CNC cutting mould were chosen. The blade and mould designs and the manufacturing processes are briefly outlined in this paper. (Author)

  16. Indirect sensorless speed control of a PMSG for wind application

    DEFF Research Database (Denmark)

    Diaz, S.A.; Silva, C.; Juliet, J.

    2009-01-01

    In this paper, the sensorless control of a permanent magnet synchronous generator (PMSG) for wind turbine applications is presented. This kind of generator has many advantages, such as: high efficiency, high power density and low maintenance requirements. To improve these characteristics in the w......In this paper, the sensorless control of a permanent magnet synchronous generator (PMSG) for wind turbine applications is presented. This kind of generator has many advantages, such as: high efficiency, high power density and low maintenance requirements. To improve these characteristics...... in the whole wind generator system a sensorless scheme is proposed, thereby avoiding problems of electromagnetic interferences and failures in the position sensor. Usually, in wind drive system, the generator is not operated a very low speeds, therefore problems related to low back-emf for flux estimation...

  17. Simulation of the Impact of New Aircraft- and Satellite-Based Ocean Surface Wind Measurements on H*Wind Analyses and Numerical Forecasts

    Science.gov (United States)

    Miller, Timothy; Atlas, Robert; Black, Peter; Chen, Shuyi; Hood, Robbie; Johnson, James; Jones, Linwood; Ruf, Chris; Uhlhorn, Eric; Krishnamurti, T. N.; hide

    2009-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath ( 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses. The H*Wind analysis, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory, brings together wind measurements from a variety of observation platforms into an objective analysis of the distribution of wind speeds in a tropical cyclone. This product is designed to improve understanding of the extent and strength of the wind field, and to improve the assessment of hurricane intensity. See http://www.aoml.noaa.gov/hrd/data_sub/wind.html. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state

  18. Hi-Q Rotor - Low Wind Speed Technology

    Energy Technology Data Exchange (ETDEWEB)

    Todd E. Mills; Judy Tatum

    2010-01-11

    The project objective was to optimize the performance of the Hi-Q Rotor. Early research funded by the California Energy Commission indicated the design might be advantageous over state-of-the-art turbines for collecting wind energy in low wind conditions. The Hi-Q Rotor is a new kind of rotor targeted for harvesting wind in Class 2, 3, and 4 sites, and has application in areas that are closer to cities, or 'load centers.' An advantage of the Hi-Q Rotor is that the rotor has non-conventional blade tips, producing less turbulence, and is quieter than standard wind turbine blades which is critical to the low-wind populated urban sites. Unlike state-of-the-art propeller type blades, the Hi-Q Rotor has six blades connected by end caps. In this phase of the research funded by DOE's Inventions and Innovation Program, the goal was to improve the current design by building a series of theoretical and numeric models, and composite prototypes to determine a best of class device. Development of the rotor was performed by aeronautical engineering and design firm, DARcorporation. From this investigation, an optimized design was determined and an 8-foot diameter, full-scale rotor was built and mounted using a Bergey LX-1 generator and furling system which were adapted to support the rotor. The Hi-Q Rotor was then tested side-by-side against the state-of-the-art Bergey XL-1 at the Alternative Energy Institute's Wind Test Center at West Texas State University for six weeks, and real time measurements of power generated were collected and compared. Early wind tunnel testing showed that the cut-in-speed of the Hi-Q rotor is much lower than a conventional tested HAWT enabling the Hi-Q Wind Turbine to begin collecting energy before a conventional HAWT has started spinning. Also, torque at low wind speeds for the Hi-Q Wind Turbine is higher than the tested conventional HAWT and enabled the wind turbine to generate power at lower wind speeds. Based on the data

  19. Comparison of NWP wind speeds and directions to measured wind speeds and directions

    DEFF Research Database (Denmark)

    Astrup, Poul; Mikkelsen, Torben

    , the NWP data from Austrian Meteorological and Geophysical Office, AMGO, cover 5th January to 31st March 2009 with two daily sets of analysis and 1 to 48 hours forecasts, the measured data cover the full three month, i.e. from 1st January, with 10 minute resolution. For the Risø site NWP results...... of the HIRLAM code from Danish Meteorological Institute were once stored for two thirds of a year, i.e. 1017 times analysis and 1 to 5 hour forecast within the period 21st October 1998 to 30th September 1999, and 10 minute averaged measured data are available since November 1995....

  20. An atlas of monthly mean distributions of GEOSAT sea surface height, SSMI surface wind speed, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1988

    Science.gov (United States)

    Halpern, D.; Zlotnicki, V.; Newman, J.; Brown, O.; Wentz, F.

    1991-01-01

    Monthly mean global distributions for 1988 are presented with a common color scale and geographical map. Distributions are included for sea surface height variation estimated from GEOSAT; surface wind speed estimated from the Special Sensor Microwave Imager on the Defense Meteorological Satellite Program spacecraft; sea surface temperature estimated from the Advanced Very High Resolution Radiometer on NOAA spacecrafts; and the Cartesian components of the 10m height wind vector computed by the European Center for Medium Range Weather Forecasting. Charts of monthly mean value, sampling distribution, and standard deviation value are displayed. Annual mean distributions are displayed.

  1. Grid impact of variable-speed wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Larsson, Aa. [Chalmers Univ. of Technology, Dept. of Electric Power Engineering, Goeteborg (Sweden); Soerensen, P. [Risoe National Lab., Roskilde (Denmark); Santjer, F. [German Wind Energy Inst., DEWI, Wilhelmshaven (Germany)

    1999-03-01

    In this paper the power quality of variable-speed wind turbines equipped with forced-commutated inverters is investigated. Measurements have been taken on the same type of variable-speed wind turbines in Germany and Sweden. The measurements have been analysed according to existing IEC standards. Special attention has been paid to the aggregation of several wind turbines on flicker emission and harmonics. The aggregation has been compared with the summation laws used in the draft IEC 61400-21 `Power Quality Requirements for Grid Connected wind turbines`. The methods for calculating and summing flicker proposed by IEC Standards are reliable. Harmonics and inter-harmonics are treated in IEC 61000-4-7 and IEC 61000-3-6. The methods for summing harmonics and inter-harmonics in IEC 61000-3-6 are applicable to wind turbines. In order to obtain a correct magnitude of the frequency components, the use of a well-defined window width, according to IEC 61000-4-7 Amendment 1 is of a great importance. (au)

  2. Fuzzy logic based variable speed wind generation system

    Energy Technology Data Exchange (ETDEWEB)

    Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.

    1996-12-31

    This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.

  3. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, M. [Brookhaven National Lab. (BNL), Upton, NY (United States); Bartholomew, M. J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Giangrande, S. [Brookhaven National Lab. (BNL), Upton, NY (United States)

    2016-03-01

    When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds on the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.

  4. 915-Mhz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, M. [Brookhaven National Laboratory (BNL), Upton, NY (United States); Bartholomew, M. J. [Brookhaven National Laboratory (BNL), Upton, NY (United States); Giangrande, S. [Brookhaven National Laboratory (BNL), Upton, NY (United States)

    2016-03-01

    When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds on the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.

  5. Very-short-term wind power probabilistic forecasts by sparse vector autoregression

    DEFF Research Database (Denmark)

    Dowell, Jethro; Pinson, Pierre

    2016-01-01

    A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...

  6. Comparison of two new short-term wind-power forecasting systems

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

    2009-07-15

    This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

  7. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    Energy Technology Data Exchange (ETDEWEB)

    Finley, Cathy [WindLogics, St. Paul, MN (United States)

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  8. Performance Investigation of A Mix Wind Turbine Using A Clutch Mechanism At Low Wind Speed Condition

    Science.gov (United States)

    Jamanun, M. J.; Misaran, M. S.; Rahman, M.; Muzammil, W. K.

    2017-07-01

    Wind energy is one of the methods that generates energy from sustainable resources. This technology has gained prominence in this era because it produces no harmful product to the society. There is two fundamental type of wind turbine are generally used this day which is Horizontal axis wind turbine (HAWT) and Vertical axis wind turbine (VAWT). The VAWT technology is more preferable compare to HAWT because it gives better efficiency and cost effectiveness as a whole. However, VAWT is known to have distinct disadvantage compared to HAWT; self-start ability and efficiency at low wind speed condition. Different solution has been proposed to solve these issues which includes custom design blades, variable angle of attack mechanism and mix wind turbine. A new type of clutch device was successfully developed in UMS to be used in a mix Savonius-Darrieus wind turbine configuration. The clutch system which barely audible when in operation compared to a ratchet clutch system interconnects the Savonius and Darrieus rotor; allowing the turbine to self-start at low wind speed condition as opposed to a standalone Darrieus turbine. The Savonius height were varied at three different size in order to understand the effect of the Savonius rotor to the mix wind turbine performance. The experimental result shows that the fabricated Savonius rotor show that the height of the Savonius rotor affecting the RPM for the turbine. The swept area (SA), aspect ratio (AR) and tip speed ratio (TSR) also calculated in this paper. The highest RPM recorded in this study is 90 RPM for Savonius rotor 0.22-meter height at 2.75 m/s. The Savonius rotor 0.22-meter also give the highest TSR for each range of speed from 0.75 m/s, 1.75 m/s and 2.75 m/s where it gives 1.03 TSR, 0.76 TSR, and 0.55 TSR.

  9. Wind generator with electronic variable-speed drives

    Energy Technology Data Exchange (ETDEWEB)

    David, A.; Buchheit, N.; Jakobsen, H.

    1996-12-31

    Variable speed drives have been inserted between the network and the generator on certain recent wind power facilities. They have the following advantages: the drive allows the wind generator to operate at low speed with a significant reduction in acoustic noise, an important point if the facilities are sited near populated areas; the drive optimizes energy transfer, providing a gain of 4 to 10 %; the drive can possibly replace certain mechanical parts (the starting system and it in some cases, the reduction gear); the drive not only provides better transient management in relation to the network for less mechanical stress on the wind generator, it is also able to control reactive power. One commercial drive design sold by several manufacturers has already been installed on several wind generators with outputs of between 150 and 600 kw. In addition, such a solution is extremely well suited to mixed renewable energy systems. This design uses two inverse rectifier type converters and can therefore exchange energy in both directions. The equivalent drive with a single IGBT converter on the motor side and a diode converter on the network side is the solution most widely adopted throughout industry (with more than 50, 000 units installed in France per year). It still remains to be seen whether such a solution could be profitable in wind generator application (since the cost of the drive is quite high). This technical analysis is more destined for the converter-machine assembly specialists and is presented in this document, paying particular attention as it does to the modelling of the `wind energy - generator - drive - network` assembly, the associated drive command and control strategies and the simulations obtained during various transients. A 7.5 kW test bed has been installed in the Laboratoire d`Electronique de Puissance de Clamart, enabling tests to be carried out which emulate the operation of a wind generator.

  10. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T; Augustus, Ellsworth H; Colonnese, Christopher P

    2003-01-01

    The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...

  11. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2004-01-01

    The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...

  12. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2005-01-01

    The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...

  13. Short-term wind power forecasting: probabilistic and space-time aspects

    DEFF Research Database (Denmark)

    Tastu, Julija

    a statistical model which would improve the quality of state-of-the-art prediction methods by accounting for the fact that forecasts errors made by such locally-optimized forecasting methods propagate in space and in time under the influence of prevailing weather conditions. Subsequently, the extension from...... work deals with the proposal and evaluation of new mathematical models and forecasting methods for short-term wind power forecasting, accounting for space-time dynamics based on geographically distributed information. Different forms of power predictions are considered, starting from traditional point...... forecasts, then extending to marginal predictive densities and, finally, considering multivariate space-time trajectories. Point predictions is the most classical approach to wind power forecasting, only providing single-valued estimates of the expected future power generation. The objective is to introduce...

  14. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

    Science.gov (United States)

    Lee, Joseph C. Y.; Lundquist, Julie K.

    2017-11-01

    Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

  15. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1 with meteorological and turbine power data

    Directory of Open Access Journals (Sweden)

    J. C. Y. Lee

    2017-11-01

    Full Text Available Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP distributed with the Weather Research and Forecasting (WRF model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

  16. Assessing noise from wind farm developments in Ireland: A consideration of critical wind speeds and turbine choice

    International Nuclear Information System (INIS)

    King, E.A.; Pilla, F.; Mahon, J.

    2012-01-01

    Wind farms are becoming increasingly popular in Ireland in an effort to increase the production of green energy within the state. As with any infrastructural development, wind farms must consider potential environmental impacts prior to construction. One particular issue that must be examined is the emission of noise from the development. In Ireland wind farm developments must adhere to planning conditions that usually outline permissible noise levels for both the construction and operational phases of the development. The critical wind speed is often cited as the wind speed at which these limits apply. This paper examines how the critical wind speed is determined and investigates its relationship with background noise levels and turbine choice. The study consisted of ten one-week monitoring periods during which meteorological conditions and background noise levels were simultaneously recorded. It was found that the critical wind speed is non-transferable, i.e. it depends on both the turbine choice and background noise environment and is specific to that particular turbine/site combination. Furthermore the critical wind speed during the night-time is often different to the overall critical wind speed suggesting that future noise studies should consider a range of critical wind speeds, particularly for night-time noise assessments. - Highlights: ► This paper considers the use of the critical wind speed when assessing noise impacts from wind farms. ► It was found that the critical wind speed could vary depending on the time of the day. ► The critical wind speed was found to be a non-transferable value. ► Noise assessments for wind farms should be developed over a range of critical wind speeds.

  17. Accuracy of National Weather Service wind-direction forecasts at Macon and Augusta, Georgia

    Science.gov (United States)

    Leonidas G. Lavdas

    1997-01-01

    National Weather Service wind forecasts and observations over a nine-year period (1985 to 1993) were analyzed to determine the usefulness of these forecasts for forestry smoke management. Data from Macon, GA indicated that forecasts were accurate to within plus or minus 22.5E about 38 percent of the time. When a wider plus or minus 67.5E window was used, accuracy...

  18. A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics

    Directory of Open Access Journals (Sweden)

    Dongran Song

    2017-05-01

    Full Text Available Variable speed wind turbines (VSWTs usually adopt a maximum power point tracking (MPPT method to optimize energy capture performance. Nevertheless, obtained performance offered by different MPPT methods may be affected by the impact of wind turbine (WT’s inertia and wind speed characteristics and it needs to be clarified. In this paper, the tip speed ratio (TSR and optimal torque (OT methods are investigated in terms of their performance under different wind speed characteristics on a 1.5 MW wind turbine model. To this end, the TSR control method based on an effective wind speed estimator and the OT control method are firstly presented. Then, their performance is investigated and compared through simulation test results under different wind speeds using Bladed software. Comparison results show that the TSR control method can capture slightly more wind energy at the cost of high component loads than the other one under all wind conditions. Furthermore, it is found that both control methods present similar trends of power reduction that is relevant to mean wind speed and turbulence intensity. From the obtained results, we demonstrate that, to further improve MPPT capability of large VSWTs, other advanced control methods using wind speed prediction information need to be addressed.

  19. AC-DC integrated load flow calculation for variable speed offshore wind farms

    DEFF Research Database (Denmark)

    Zhao, Menghua; Chen, Zhe; Blaabjerg, Frede

    2005-01-01

    This paper proposes a sequential AC-DC integrated load flow algorithm for variable speed offshore wind farms. In this algorithm, the variable frequency and the control strategy of variable speed wind turbine systems are considered. In addition, the losses of wind turbine systems and the losses...... of converters are also integrated into the load flow algorithm. As a general algorithm, it can be applied to different types of wind farm configurations, and the load flow is related to the wind speed....

  20. Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models

    Directory of Open Access Journals (Sweden)

    Hao Chen

    2014-07-01

    Full Text Available The scientific evaluation methodology for the forecast accuracy of wind power forecasting models is an important issue in the domain of wind power forecasting. However, traditional forecast evaluation criteria, such as Mean Squared Error (MSE and Mean Absolute Error (MAE, have limitations in application to some degree. In this paper, a modern evaluation criterion, the Diebold-Mariano (DM test, is introduced. The DM test can discriminate the significant differences of forecasting accuracy between different models based on the scheme of quantitative analysis. Furthermore, the augmented DM test with rolling windows approach is proposed to give a more strict forecasting evaluation. By extending the loss function to an asymmetric structure, the asymmetric DM test is proposed. Case study indicates that the evaluation criteria based on DM test can relieve the influence of random sample disturbance. Moreover, the proposed augmented DM test can provide more evidence when the cost of changing models is expensive, and the proposed asymmetric DM test can add in the asymmetric factor, and provide practical evaluation of wind power forecasting models. It is concluded that the two refined DM tests can provide reference to the comprehensive evaluation for wind power forecasting models.

  1. From probabilistic forecasts to statistical scenarios of short-term wind power production

    DEFF Research Database (Denmark)

    Pinson, Pierre; Papaefthymiou, George; Klockl, Bernd

    2009-01-01

    on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multistage decision-making problems, e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures...

  2. Coronal hole evolution from multi-viewpoint data as input for a STEREO solar wind speed persistence model

    Directory of Open Access Journals (Sweden)

    Temmer Manuela

    2018-01-01

    Full Text Available We present a concept study of a solar wind forecasting method for Earth, based on persistence modeling from STEREO in situ measurements combined with multi-viewpoint EUV observational data. By comparing the fractional areas of coronal holes (CHs extracted from EUV data of STEREO and SoHO/SDO, we perform an uncertainty assessment derived from changes in the CHs and apply those changes to the predicted solar wind speed profile at 1 AU. We evaluate the method for the time period 2008–2012, and compare the results to a persistence model based on ACE in situ measurements and to the STEREO persistence model without implementing the information on CH evolution. Compared to an ACE based persistence model, the performance of the STEREO persistence model which takes into account the evolution of CHs, is able to increase the number of correctly predicted high-speed streams by about 12%, and to decrease the number of missed streams by about 23%, and the number of false alarms by about 19%. However, the added information on CH evolution is not able to deliver more accurate speed values for the forecast than using the STEREO persistence model without CH information which performs better than an ACE based persistence model. Investigating the CH evolution between STEREO and Earth view for varying separation angles over ∼25–140° East of Earth, we derive some relation between expanding CHs and increasing solar wind speed, but a less clear relation for decaying CHs and decreasing solar wind speed. This fact most likely prevents the method from making more precise forecasts. The obtained results support a future L5 mission and show the importance and valuable contribution using multi-viewpoint data.

  3. Coronal hole evolution from multi-viewpoint data as input for a STEREO solar wind speed persistence model

    Science.gov (United States)

    Temmer, Manuela; Hinterreiter, Jürgen; Reiss, Martin A.

    2018-03-01

    We present a concept study of a solar wind forecasting method for Earth, based on persistence modeling from STEREO in situ measurements combined with multi-viewpoint EUV observational data. By comparing the fractional areas of coronal holes (CHs) extracted from EUV data of STEREO and SoHO/SDO, we perform an uncertainty assessment derived from changes in the CHs and apply those changes to the predicted solar wind speed profile at 1 AU. We evaluate the method for the time period 2008-2012, and compare the results to a persistence model based on ACE in situ measurements and to the STEREO persistence model without implementing the information on CH evolution. Compared to an ACE based persistence model, the performance of the STEREO persistence model which takes into account the evolution of CHs, is able to increase the number of correctly predicted high-speed streams by about 12%, and to decrease the number of missed streams by about 23%, and the number of false alarms by about 19%. However, the added information on CH evolution is not able to deliver more accurate speed values for the forecast than using the STEREO persistence model without CH information which performs better than an ACE based persistence model. Investigating the CH evolution between STEREO and Earth view for varying separation angles over ˜25-140° East of Earth, we derive some relation between expanding CHs and increasing solar wind speed, but a less clear relation for decaying CHs and decreasing solar wind speed. This fact most likely prevents the method from making more precise forecasts. The obtained results support a future L5 mission and show the importance and valuable contribution using multi-viewpoint data.

  4. Design of a shrouded wind turbine for low wind speeds / Jacobus Daniel Human

    OpenAIRE

    Human, Jacobus Daniel

    2014-01-01

    The use of renewable energy is promoted worldwide to be less dependent on fossil fuels and nuclear energy. Therefore research in the field is driven to increase efficiency of renewable energy systems. This study aimed to develop a wind turbine for low wind speeds in South Africa. Although there is a greater tendency to use solar panels because of the local weather conditions, there are some practical implications that have put the use of solar panels in certain areas to an end....

  5. Combination of Deterministic and Probabilistic Meteorological Models to enhance Wind Farm Power Forecasts

    International Nuclear Information System (INIS)

    Bremen, Lueder von

    2007-01-01

    Large-scale wind farms will play an important role in the future worldwide energy supply. However, with increasing wind power penetration all stakeholders on the electricity market will ask for more skilful wind power predictions regarding save grid integration and to increase the economic value of wind power. A Neural Network is used to calculate Model Output Statistics (MOS) for each individual forecast model (ECMWF and HIRLAM) and to model the aggregated power curve of the Middelgrunden offshore wind farm. We showed that the combination of two NWP models clearly outperforms the better single model. The normalized day-ahead RMSE forecast error for Middelgrunden can be reduced by 1% compared to single ECMWF. This is a relative improvement of 6%. For lead times >24h it is worthwhile to use a more sophisticated model combination approach than simple linear weighting. The investigated principle component regression is able to extract the uncorrelated information from two NWP forecasts. The spread of Ensemble Predictions is related to the skill of wind power forecasts. Simple contingency diagrams show that low spread corresponds is more often related to low forecast errors and high spread to large forecast errors

  6. Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

    Directory of Open Access Journals (Sweden)

    Dongxiao Niu

    2013-01-01

    Full Text Available Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM combined with rough set theory clustering technique (RST is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP. Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP. The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting.

  7. Velocity Measurement Systems for a Low-speed Wind Tunnel

    Science.gov (United States)

    2015-04-29

    SECURITY CLASSIFICATION OF: Funds were provided by the ARO for the purchase of TSI hot-wire anemometer equipment and a Dantec particle- image...Velocity Measurement Systems for a Low-speed Wind Tunnel Report Title Funds were provided by the ARO for the purchase of TSI hot-wire anemometer equipment...Funds were provided by the Army Research Office for the purchase of TSI hot-wire anemometer equipment and a Dantec particle-image velocimetry system

  8. Electron characteristics in the high speed solar wind

    International Nuclear Information System (INIS)

    Feldman, W.C.

    1978-01-01

    Experimental work done since 1976 on the physics of electrons in the high speed solar wind is reviewed. The main new results are most electron parameters are uniform in the high speed solar wind indicating that it is a well defined, structure-free state of the coronal expansion. The higher energy unbound part of electron velocity distributions (the halo) is consistent with nearly collisionless propagation to 1AU from some heliocentric distance in the range between about 10 and 30 solar radii. The low energy bound electron (core) component appears to be strongly coupled to the protons as well as to one another through Coulomb and wave electron collisions. The first measured radial profile of the core-electron temperature in the high speed solar wind is best characterized in terms of two separate power laws applicable in the distance ranges between 0.47 and 0.62 AU and between 0.62 and 1.0 AU respectively. The best estimate for the power law indices in the inner and outer regions are α 1 = -1.14 +-0.24 and α 0 = +0.28 +-0.13, respectively. A relations of the form Q = γN/sub c/kT/sub c/U/(1 + βγ/sub sigma11 γcp) with = 10.7 and β = 4.2 may be useful in closing the Vlasov moment equations describing general solar wind flows in interplanetary space. The quantity Q is the total heat flux, N/sub c/ and T/sub c/ are the core-electron density and temperature respectively, k is Boltzmann's constant, U is the proton bulk speed in the solar corotating reference frame, /sub tsigma/ is the bounce period of a typical core electron and /+ sub tcp/ is the average core electron-proton Coulomb deflection time. 16 refs

  9. Accounting for the speed shear in wind turbine power performance measurement

    DEFF Research Database (Denmark)

    Wagner, Rozenn; Courtney, Michael; Gottschall, Julia

    2011-01-01

    The current IEC standard for wind turbine power performance measurement only requires measurement of the wind speed at hub height assuming this wind speed to be representative for the whole rotor swept area. However, the power output of a wind turbine depends on the kinetic energy flux, which...

  10. Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

    International Nuclear Information System (INIS)

    Zhang, Yao; Wang, Jianxue; Luo, Xu

    2015-01-01

    Highlights: • Quantitative information on the uncertainty of wind power generation. • Kernel density estimator provides non-Gaussian predictive distributions. • Logarithmic transformation reduces the skewness of wind power density. • Boundary kernel method eliminates the density leakage near the boundary. - Abstracts: Probabilistic wind power forecasting not only produces the expectation of wind power output, but also gives quantitative information on the associated uncertainty, which is essential for making better decisions about power system and market operations with the increasing penetration of wind power generation. This paper presents a novel kernel density estimator for probabilistic wind power forecasting, addressing two characteristics of wind power which have adverse impacts on the forecast accuracy, namely, the heavily skewed and double-bounded nature of wind power density. Logarithmic transformation is used to reduce the skewness of wind power density, which improves the effectiveness of the kernel density estimator in a transformed scale. Transformations partially relieve the boundary effect problem of the kernel density estimator caused by the double-bounded nature of wind power density. However, the case study shows that there are still some serious problems of density leakage after the transformation. In order to solve this problem in the transformed scale, a boundary kernel method is employed to eliminate the density leak at the bounds of wind power distribution. The improvement of the proposed method over the standard kernel density estimator is demonstrated by short-term probabilistic forecasting results based on the data from an actual wind farm. Then, a detailed comparison is carried out of the proposed method and some existing probabilistic forecasting methods

  11. An atlas of monthly mean distributions of SSMI surface wind speed, AVHRR/2 sea surface temperature, AMI surface wind velocity, TOPEX/POSEIDON sea surface height, and ECMWF surface wind velocity during 1993

    Science.gov (United States)

    Halpern, D.; Fu, L.; Knauss, W.; Pihos, G.; Brown, O.; Freilich, M.; Wentz, F.

    1995-01-01

    The following monthly mean global distributions for 1993 are presented with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States (U.S.) Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the Advanced Very High Resolution Radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) satellite; 10-m height wind speed and direction estimated from the Active Microwave Instrument (AMI) on the European Space Agency (ESA) European Remote Sensing (ERS-1) satellite; sea surface height estimated from the joint U.S.-France Topography Experiment (TOPEX)/POSEIDON spacecraft; and 10-m height wind speed and direction produced by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of annual mean, monthly mean, and sampling distributions are displayed.

  12. Two-Step Forecast of Geomagnetic Storm Using Coronal Mass Ejection and Solar Wind Condition

    Science.gov (United States)

    Kim, R.-S.; Moon, Y.-J.; Gopalswamy, N.; Park, Y.-D.; Kim, Y.-H.

    2014-01-01

    To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz = -5 nT or Ey = 3 mV/m for t = 2 h for moderate storms with minimum Dst less than -50 nT) (i.e. Magnetic Field Magnitude, B (sub z) less than or equal to -5 nanoTeslas or duskward Electrical Field, E (sub y) greater than or equal to 3 millivolts per meter for time greater than or equal to 2 hours for moderate storms with Minimum Disturbance Storm Time, Dst less than -50 nanoTeslas) and a Dst model developed by Temerin and Li (2002, 2006) (TL [i.e. Temerin Li] model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90 percent) than the forecasts based on the TL model (87 percent). However, the latter produces better forecasts for 24 nonstorm events (88 percent), while the former correctly forecasts only 71 percent of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80 percent) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (n, i.e. cap operator - the intersection set that is comprised of all the elements that are common to both), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81 percent) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (?, i.e. cup operator - the union set that is comprised of all the elements of either or both

  13. Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

    DEFF Research Database (Denmark)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2018-01-01

    the advantage of being able to produce spatially out-of-sample forecasts. We use a Bayesian hierarchical framework to obtain fast and accurate forecasts of wind power generation not only at wind farms where recent data are available but also at a larger portfolio including wind farms without recent observations...

  14. An innovative medium speed wind turbine rotor blade design for low wind regime (electrical power generation)

    International Nuclear Information System (INIS)

    Abas Abd Wahab; Chong Wen Tong

    2001-01-01

    This paper describes the preliminary study of a small-scale wind turbine rotor blade (a low wind speed region turbine). A new wind turbine rotor blade (AE2 blade) for stand alone system has been conceptualized, designed, constructed and tested. The system is a reduced size prototype (half-scaled) to develop an efficient (adapted to Malaysian wind conditions)and cost effective wind energy conversion system (WECS) with local design and production technique. The blades were constructed from aluminium sheet with metal blending technique. The layout and design of rotor blade, its innovative features and test results are presented. Results from indoor test showed that the advantages of AE2 blade in low speed, with the potential of further improvements. The best rotor efficiency, C P attained with simple AE2 blades rotor (number of blade = 3) was 37.3% (Betz efficiency = 63%) at tip speed ratio (TSR) = 3.6. From the fabrication works and indoor testing, the AE2 blade rotor has demonstrated its structural integrity (ease of assembly and transportation), simplicity, acceptable performance and low noise level. (Author)

  15. Relationship of oceanic whitecap coverage to wind speed and wind history

    NARCIS (Netherlands)

    Callaghan, A.; Leeuw, G. de; Cohen, L.; O'Dowd, C.D.

    2008-01-01

    Sea surface images obtained during the 2006 Marine Aerosol Production (MAP) campaign in the North East Atlantic were analysed for values of percentage whitecap coverage (W). Values of W are presented for wind speeds up to circa 23 m s-1. The W data were divided into two overlapping groups and a

  16. A Combined Reliability Model of VSC-HVDC Connected Offshore Wind Farms Considering Wind Speed Correlation

    DEFF Research Database (Denmark)

    Guo, Yifei; Gao, Houlei; Wu, Qiuwei

    2017-01-01

    and WTGs outage. The wind speed correlation between different WFs is included in the two-dimensional multistate WF model by using an improved k-means clustering method. Then, the entire system with two WFs and a threeterminal VSC-HVDC system is modeled as a multi-state generation unit. The proposed model...

  17. Wind power forecasting system EOlienne SPEO : development, preliminary results and integration at Hydro-Quebec

    Energy Technology Data Exchange (ETDEWEB)

    Forcione, A.; Roberge, G. [Hydro-Quebec, Varennes, PQ (Canada). IREQ; Yu, W.; Glazer, A.; Benoit, R.; Plante, A.; Tran, L.D.; Chardon, L. [Environment Canada, Ottawa, ON (Canada)

    2007-07-01

    Wind generation forecasting at Hydro-Quebec was discussed with particular reference to the collaborative efforts between the utility's Research Institute and Environment Canada in developing the Systeme de Prevision EOlienne (SPEO). The European ANEMOS platform was installed at Hydro-Quebec Distribution in 2006. Operational forecasts using the Global Environmental Multi-scale model (GEM) from the Canadian Meteorological Centre served as input for SPEO. This presentation evaluated the performance of the forecasting model, and presented best approaches for long term use and continuous improvement. SPEO was developed to forecast wind and other atmospheric variables, and not generated power. The development of the software began in September 2006 with the development and integration of necessary components, followed by the calibration of the system, 15 months of operational forecasts, experimentation and final analysis in 2008. The GEM-global model provides 10 days and 240 hours of hourly forecasts with 35 km resolution, while the GEM-regional model provides 2 days and 48 hours of hourly forecasts with 15 km resolution. It was shown that the development of a good forecasting system depends entirely on the availability of a maximum number of observation sources, which for SPEO includes 13 Environment Canada stations and wind farm masts. The final value of a wind forecasting system also depends on compatibility with the electric system management tools and processes. Research is ongoing to improve SPEO through validation tools, integration of newly available observations, recalibration and experimentation. Future tasks will be to extend the 48 hour horizon, to optimize the number crunching efficiency and to characterize wind farms more precisely. figs.

  18. Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

    Directory of Open Access Journals (Sweden)

    Hai Lan

    2017-02-01

    Full Text Available Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN and hybrid multi-objective particle swarm optimization (HMOPSO to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

  19. Winding Losses in High-Speed Machines using Form-Wound Windings

    Science.gov (United States)

    Zhang, Wanjun

    Understanding the ac loss phenomena in form-wound windings is critical for achieving high efficiency in ac machines that employ this type of winding. Accurate calculation of these losses using finite element (FE) analysis typically requires a fine mesh size in the conductors and small time steps, requiring considerable computational resources to accomplish. This research program presents the development of a closed-form 2D analytical model that is capable of calculating the ac losses in form-wound windings with promising accuracy and short computation times. This model is valuable for carrying out rapid assessments of the ac losses in machines for a wide range of operating conditions, making it practical to evaluate large numbers of candidate designs. Significant attention is devoted to exploring alternative approaches for reducing these ac losses that are influenced by many winding design factors including the conductor locations and thicknesses, number of conductors per slot, and phase arrangement. In addition, experimental tests have been carried out using three identical stators with form-wound, "pseudo" Litz and true Litz windings, all configured with the same winding function. The availability of these stators makes it possible to experimentally segregate the winding losses, explore winding losses under different operating conditions, and, finally, build confidence in the proposed model. The results of this investigation highlight the advantages of form-wound windings for low-frequency operation while also clearly demonstrating the risks that they present for unacceptably high ac losses at elevated frequencies. This work also demonstrates that careful attention to the design details of form-wound windings can lead to promising reductions of the ac winding losses under demanding operating conditions associated with high-speed operation.

  20. FORECASTING OF PASSENGER TRAFFIC UPON IMPLEMENTATION OF HIGH-SPEED RUNNING

    Directory of Open Access Journals (Sweden)

    M. B. Kurhan

    2017-02-01

    Full Text Available Purpose. Forecasting of passenger traffic flows in the future is an essential and integral part of the complex process of designing of high-speed network (HSN. HSN direction and its parameters are determined by the volume of passenger traffic, the estimated value of which depends on the economic performance of the country, as well as the material status of citizens living in HSN concentration area, transport mobility of population, development of competing modes of transport and so on. The purpose of this work is to analyse the existing methods of passenger traffic forecasting, to evaluate errors of the existing models concerning determination of traffic volumes and to specify the scientific approach to the development of high-speed rail transport in Ukraine. Methodology. The existing forecasting methods are reduced to the following ones: Delphi approach, extrapolation method, factor and correlation analysis, simulation method. The method described in this paper is based on scientific approaches such as analysis – a comprehensive and detailed study of various aspects of the known forecasting methods, comparing of existing methods for establishing differences and similarities, as well as deduction – use of general knowledge to get the new particular one. Thus, the unified indicators determined for the country as a whole, such as gross domestic product, national income, total population and others cannot be used to forecast the traffic flow on specific areas of HSN construction. Therefore, it is necessary to move from the overall forecast to traffic volume forecast on particular direction. Findings. The conclusions are derived from the analysis of different approaches and methods of passenger flow forecasting. It is proposed to create typical techniques of traffic flow forecasting using modern mathematical methods that would allow avoiding unreasonable decisions and shortening project development time. The resulting recommendations will help

  1. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    Energy Technology Data Exchange (ETDEWEB)

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability

  2. Wind speed and wind energy potentials in EURO-CORDEX ensemble simulations: evaluation, bias-correction and future changes

    Science.gov (United States)

    Moemken, Julia; Reyers, Mark; Feldmann, Hendrik; Pinto, Joaquim G.

    2017-04-01

    The EURO-CORDEX initiative aims at dynamically downscaling the CMIP5 global climate projections to provide an ensemble of high-resolution regional climate change scenarios for Europe. We analyse a multi-model ensemble of recent EURO-CORDEX simulations at 12km resolution focussing on wind speed and wind energy potentials. The analysis is based on 3-hourly 10m wind speeds from 9 different GCM-RCM-chains. For validation, the historical 10m wind speeds are compared to ERA-Interim driven evaluation runs for the same RCMs. This comparison uncovered some substantial biases for wind speeds, which result both from the choice of GCM and RCM. Since these biases may influence the climate change signal, the 10m wind speeds from the historical and the scenario runs are bias corrected. With this aim, a probability mapping is carried out to adjust the simulated wind speeds to the evaluation runs. In a next step, the corrected 10m wind speeds are extrapolated to the average hub height of a wind turbine (here 100m). For this purpose, different approximations for the power law exponent and their influence on the wind speed distribution in 100m were investigated. Finally, gridded wind energy output (Eout) is calculated for two operational wind turbines by taking the specific characteristics of the turbines into account. With this methodology, future changes of wind characteristics relevant for the wind energy production are estimated, including mean changes in annual and seasonal wind energy production, changes in variability and extreme events like long-lasting calm periods.

  3. Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • Four algorithms [EMD/FEEMD/WD/WPD] are proposed for the wind speed decomposition. • Two new hybrid forecasting algorithms [FEEMD-MLP/ANFIS] are presented. • The contributions of the FEEMD/WPD algorithms are both significant. • The MLP has better forecasting performance than the ANFIS in these cases. • All the proposed hybrid algorithms are suitable for the wind speed predictions. - Abstract: The technology of wind speed prediction is important to guarantee the safety of wind power utilization. Compared to the single algorithms, the hybrid ones always have better performance in the wind speed predictions. In this paper, three most important decomposing algorithms [Wavelet Decomposition – WD/Wavelet Packet Decomposition – WPD/Empirical Mode Decomposition – EMD] and a latest decomposing algorithm [Fast Ensemble Empirical Mode Decomposition – FEEMD] are all adopted to realize the wind speed high-precision predictions with two representative networks [MLP Neural Network/ANFIS Neural Network]. Based on the hybrid forecasting framework, two new wind speed forecasting methods [FEEMD-MLP and FEEMD-ANFIS] are proposed. Additionally, a series of performance comparison is provided, which includes EMD-MLP, FEEMD-MLP, EDM-ANFIS, FEEMD-ANFIS, WD-MLP, WD-ANFIS, WPD-MLP and WPD-ANFIS. The aim of the study is to investigate the decomposing and forecasting performance of the different hybrid models. Two experimental results show that: (1) Due to the inclusion of the decomposing algorithms, the hybrid ANN algorithms have better performance than their corresponding single ANN algorithms; (2) the proposed new FEEMD-MLP hybrid model has the best performance in the three-step predictions while the WPD-MLP hybrid model has the best performance in the one-step predictions; (3) among the decomposing algorithms, the FEEMD and WPD have better performance than the EMD and WD, respectively; (4) in the forecasting neural networks, the MLP has better performance

  4. Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

    Directory of Open Access Journals (Sweden)

    Radziukynas V.

    2016-04-01

    Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.

  5. On the Relationship Between High Speed Solar Wind Streams and Radiation Belt Electron Fluxes

    Science.gov (United States)

    Zheng, Yihua

    2011-01-01

    Both past and recent research results indicate that solar wind speed has a close connection to radiation belt electron fluxes [e.g., Paulikas and Blake, 1979; Reeves et aI., 2011]: a higher solar wind speed is often associated with a higher level of radiation electron fluxes. But the relationship can be very complex [Reeves et aI., 2011]. The study presented here provides further corroboration of this viewpoint by emphasizing the importance of a global perspective and time history. We find that all the events during years 2010 and 2011 where the >0.8 MeV integral electron flux exceeds 10(exp 5) particles/sq cm/sr/s (pfu) at GEO orbit are associated with the high speed streams (HSS) following the onset of the Stream Interaction Region (SIR), with most of them belonging to the long-lasting Corotating Interaction Region (CIR). Our preliminary results indicate that during HSS events, a maximum speed of 700 km/s and above is a sufficient but not necessary condition for the > 0.8 MeV electron flux to reach 10(exp 5) pfu. But in the exception cases of HSS events where the electron flux level exceeds the 10(exp 5) pfu value but the maximum solar wind speed is less than 700 km/s, a prior impact can be noted either from a CME or a transient SIR within 3-4 days before the arrival of the HSS - stressing the importance of time history. Through superposed epoch analysis and studies providing comparisons with the CME events and the HSS events where the flux level fails to reach the 10(exp 5) pfu, we will present the quantitative assessment of behaviors and relationships of various quantities, such as the time it takes to reach the flux threshold value from the stream interface and its dependence on different physical parameters (e.g., duration of the HSS event, its maximum or average of the solar wind speed, IMF Bz, Kp). The ultimate goal is to apply what is derived to space weather forecasting.

  6. 9x15 Low Speed Wind Tunnel Acoustic Improvements

    Science.gov (United States)

    Stark, David; Stephens, David

    2016-01-01

    The 9- by 15-Foot Low Speed Wind Tunnel (9x15 LSWT) at NASA Glenn Research Center was built in 1969 in the return leg of the 8- by 6-Foot Supersonic Wind Tunnel (8x6 SWT). The 8x6 SWT was completed in 1949 and acoustically treated to mitigate community noise issues in 1950. This treatment included the addition of a large muffler downstream of the 8x6 SWT test section and diffuser. The 9x15 LSWT was designed for performance testing of VSTOL aircraft models, but with the addition of the current acoustic treatment in 1986 the tunnel has been used principally for acoustic and performance testing of aircraft propulsions systems. The present document describes an anticipated acoustic upgrade to be completed in 2017.

  7. The economic benefit of short-term forecasting for wind energy in the UK electricity market

    International Nuclear Information System (INIS)

    Barthelmie, R.J.; Murray, F.; Pryor, S.C.

    2008-01-01

    In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around pound 5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators. (author)

  8. LIDAR and SODAR Measurements of Wind Speed and Direction in Upland Terrain for Wind Energy Purposes

    Directory of Open Access Journals (Sweden)

    Eamon McKeogh

    2011-08-01

    Full Text Available Detailed knowledge of the wind resource is necessary in the developmental and operational stages of a wind farm site. As wind turbines continue to grow in size, masts for mounting cup anemometers—the accepted standard for resource assessment—have necessarily become much taller, and much more expensive. This limitation has driven the commercialization of two remote sensing (RS tools for the wind energy industry: The LIDAR and the SODAR, Doppler effect instruments using light and sound, respectively. They are ground-based and can work over hundreds of meters, sufficient for the tallest turbines in, or planned for, production. This study compares wind measurements from two commercial RS instruments against an instrumented mast, in upland (semi-complex terrain typical of where many wind farms are now being installed worldwide. With appropriate filtering, regression analyses suggest a good correlation between the RS instruments and mast instruments: The RS instruments generally recorded lower wind speeds than the cup anemometers, with the LIDAR more accurate and the SODAR more precise.

  9. Vertical evolution of wind meandering in a nocturnal boundary layer during low-wind speed conditions

    Science.gov (United States)

    Stefanello, Michel; Acevedo, Otávio; Mortarini, Luca; Cava, Daniela; Giostra, Umberto; Degrazia, Gervásio; Anfossi, Domenico

    2017-04-01

    In the nocturnal boundary layer episodes of horizontal wind meandering are frequent. These episodes are characterised by low-wind regimes (wind speed less than 1.5 m s-1) in which submeso motions drive the wind dynamics and turbulence is weak and often intermittent. The inception of the meandering phenomenon as well as the interaction between turbulence and the submeso oscillations are still poorly understood. In this work we study the vertical evolution of the wind meandering by analysingnight-time anemometric data. The observations were carried on at a coastal site in Espirito Santo state, south-eastern Brazil from august to November 2016. The turbulent data, divided in hourly series, were collected in a 140-m tower designed to provide micrometeorological observations with high vertical resolution and deep coverage of the lower portion of the atmospheric boundary layer. Particularly, turbulence observations of the wind components and temperature are carried at 11 vertical levels. The tower has been deployed next to a natural gas power plant, at 3 km from the ocean. The terrain is generally flat for an area of 30 km from the tower, where moderate hills exist. The meandering timescale at each level is evaluated through the Eulerian Autocorrelation Functions of the horizontal wind velocity components and temperature, while the interactions between the different scales of motions is studied using the multi-correlation analysis. Thus the vertical evolution of meandering and time scales structure can be studied.

  10. Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

    Directory of Open Access Journals (Sweden)

    Wei-Kuang Lai

    2016-02-01

    Full Text Available Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD are proposed to analyze the signals (e.g., handovers (HOs, call arrivals (CAs, normal location updates (NLUs and periodic location updates (PLUs from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD. In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.

  11. A hybrid wind power forecasting model based on data mining and wavelets analysis

    International Nuclear Information System (INIS)

    Azimi, R.; Ghofrani, M.; Ghayekhloo, M.

    2016-01-01

    Highlights: • An improved version of K-means algorithm is proposed for clustering wind data. • A persistence based method is applied to select the best cluster for NN training. • A combination of DWT and HANTS methods is used to provide a deep learning for NN. • A hybrid of T.S.B K-means, DWT and HANTS and NN is developed for wind forecasting. - Abstract: Accurate forecasting of wind power plays a key role in energy balancing and wind power integration into the grid. This paper proposes a novel time-series based K-means clustering method, named T.S.B K-means, and a cluster selection algorithm to better extract features of wind time-series data. A hybrid of T.S.B K-means, discrete wavelet transform (DWT) and harmonic analysis time series (HANTS) methods, and a multilayer perceptron neural network (MLPNN) is developed for wind power forecasting. The proposed T.S.B K-means classifies data into separate groups and leads to more appropriate learning for neural networks by identifying anomalies and irregular patterns. This improves the accuracy of the forecast results. A cluster selection method is developed to determine the cluster that provides the best training for the MLPNN. This significantly accelerates the forecast process as the most appropriate portion of the data rather than the whole data is used for the NN training. The wind power data is decomposed by the Daubechies D4 wavelet transform, filtered by the HANTS, and pre-processed to provide the most appropriate inputs for the MLPNN. Time-series analysis is used to pre-process the historical wind-power generation data and structure it into input-output series. Wind power datasets with diverse characteristics, from different wind farms located in the United States, are used to evaluate the accuracy of the hybrid forecasting method through various performance measures and different experiments. A comparative analysis with well-established forecasting models shows the superior performance of the proposed

  12. Soil tillage and windbreak effects on millet and cowpea: I. Wind speed, evaporation, and wind erosion

    Energy Technology Data Exchange (ETDEWEB)

    Banzhaf, J.; Leihner, D.E.; Buerkert, A. (Univ. of Hohenheim, Stuttgart (Germany)); Serafini, P.G. (Univ. of Arkansas, Fayetteville (United States))

    Deforestation, overgrazing, and declining soil regeneration periods have resulted in increased wind erosion problems in dry areas of the West African Sahel, but little is known about the bio-physical factors involved. This research was conducted to determine the effects of ridging and four different windbreak spacings on wind erosion, potential evaporation, and soil water reserves. A field trial was conducted from 1985 to 1987 on 12 ha of a Psammentic Paleustalf in Southern Niger. Millet, Pennisetum glaucum (L.), and cowpea, Vigna unguiculata (L.) Walp., were seeded in strips on flat and ridged soil. Windbreaks of savannah vegetation were spaced at 6, 20, 40, and 90 m. The effects of ridging on wind speed, evaporation, and wind erosion were small and mostly non-significant. However, average wind speed at 0.3 m above ground in the center of cowpea and millet strips was significantly reduced from 2.8 to 2.1 m s[sup [minus]1] as windbreak distances narrowed from 90 to 6 m. As a consequence, potential evaporation declined by 15% and the amount of windblown soil particles by 50% in ridged and by 70% in flat treatments. Despite reduced potential evaporation, average subsoil water reserves were 14 mm smaller in the 6- than in the 20-m windbreak spacing indicating excessive water extraction by the windbreak vegetation. Thus, establishing windbreaks with natural savannah vegetation may require a careful consideration of the agronomic benefits and costs to competing crops. 21 refs., 5 figs.

  13. Soil tillage and windbreak effects on millet and cowpea: I. Wind speed, evaporation, and wind erosion

    International Nuclear Information System (INIS)

    Banzhaf, J.; Leihner, D.E.; Buerkert, A.; Serafini, P.G.

    1992-01-01

    Deforestation, overgrazing, and declining soil regeneration periods have resulted in increased wind erosion problems in dry areas of the West African Sahel, but little is known about the bio-physical factors involved. This research was conducted to determine the effects of ridging and four different windbreak spacings on wind erosion, potential evaporation, and soil water reserves. A field trial was conducted from 1985 to 1987 on 12 ha of a Psammentic Paleustalf in Southern Niger. Millet, Pennisetum glaucum (L.), and cowpea, Vigna unguiculata (L.) Walp., were seeded in strips on flat and ridged soil. Windbreaks of savannah vegetation were spaced at 6, 20, 40, and 90 m. The effects of ridging on wind speed, evaporation, and wind erosion were small and mostly non-significant. However, average wind speed at 0.3 m above ground in the center of cowpea and millet strips was significantly reduced from 2.8 to 2.1 m s -1 as windbreak distances narrowed from 90 to 6 m. As a consequence, potential evaporation declined by 15% and the amount of windblown soil particles by 50% in ridged and by 70% in flat treatments. Despite reduced potential evaporation, average subsoil water reserves were 14 mm smaller in the 6- than in the 20-m windbreak spacing indicating excessive water extraction by the windbreak vegetation. Thus, establishing windbreaks with natural savannah vegetation may require a careful consideration of the agronomic benefits and costs to competing crops. 21 refs., 5 figs

  14. Probabilistic wind power forecasting with online model selection and warped gaussian process

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

    2014-01-01

    Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

  15. Simulation Model of an Active-stall Fixed-speed Wind Turbine Controller

    DEFF Research Database (Denmark)

    Jauch, Clemens; Hansen, Anca D.; Soerensen, Poul

    2004-01-01

    This paper describes an active-stall wind turbine controller. The objective is to develop a general model of an active stall controller in order to simulate the operation of grid connected active stall wind turbines. The active stall turbine concept and its control strategies are presented...... and evaluated by simulations. The presented controller is described for continuous operation under all wind speeds from start-up wind speed to shut down wind speed. Due to its parametric implementation it is general i.e. it can represent different active stall wind turbine controllers and can be implemented...

  16. Simulation model of an active-stall fixed-speed wind turbine controller

    Energy Technology Data Exchange (ETDEWEB)

    Jauch, C.; Hansen, A.D.; Sorensen, P.; Blaabjerg, F.

    2004-07-01

    This paper describes an active-stall wind turbine controller. The objective is to develop a general model of an active stall controller in order to simulate the operation of grid connected active stall wind turbines. The active stall turbine concept and its control strategies are presented and evaluated by simulations. The presented controller is described for continuous operation under all wind speeds from start-up wind speed to shut down wind speed. Due to its parametric implementation it is general i. e. it can represent different active stall wind turbine controllers and can be implemented in different simulation tools. (author)

  17. Low Voltage Ride-Through of Variable Speed Wind Turbines with Permanent Magnet Synchronous Generator

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Wang, Yue

    2009-01-01

    This paper presents a simulation model of a MW-level variable speed wind turbine with a permanent magnet synchronous generator (PMSG) and a full-scale converter developed in the simulation tool of PSCAD/EMTDC. The low voltage ride-through (LVRT) capability of the wind turbine is investigated. A new...... speed wind turbines with PMSG....

  18. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  19. Evaluation of different operational strategies for lithium ion battery systems connected to a wind turbine for primary frequency regulation and wind power forecast accuracy improvement

    DEFF Research Database (Denmark)

    Swierczynski, Maciej Jozef; Stroe, Daniel Ioan; Stan, Ana-Irina

    2012-01-01

    High penetration levels of variable wind energy sources can cause problems with their grid integration. Energy storage systems connected to wind turbine/wind power plants can improve predictability of the wind power production and provide ancillary services to the grid. This paper investigates...... economics of different operational strategies for Li-ion systems connected to wind turbines for wind power forecast accuracy improvement and primary frequency regulation....

  20. Application of Boost Converter to Increase the Speed Range of Dual-stator Winding Induction Generator in Wind Power Systems

    DEFF Research Database (Denmark)

    Kavousi, Ayoub; Fathi, S. Hamid; Milimonfared, Jafar

    2018-01-01

    In this paper, a topology using a Dual-stator Winding Induction Generator (DWIG) and a boost converter is proposed for the variable speed wind power application. At low rotor speeds, the generator saturation limits the voltage of the DWIG. Using a boost converter, higher DC voltage can be produce...

  1. High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems

    DEFF Research Database (Denmark)

    Delikaraoglou, Stefanos; Pinson, Pierre

    2014-01-01

    The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires......-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model....... high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single...

  2. Validation and development of existing and new RAOB-based warm-season convective wind forecasting tools for Cape Canaveral Air Force Station and Kennedy Space Center

    Science.gov (United States)

    McCue, Mitchell Hollis

    these techniques except the ensemble CART methods were built with data from the 1995 to 2007 warm-seasons and validated with a separate independent dataset from the 2008 and 2009 warm-seasons. Ensemble CART models were built using randomly selected data from the 1995 to 2009 RAOB dataset and validated with data not used in constructing the models. Three different ensemble CART algorithms including the random forests, bagging, and boosting algorithms were tested to find the best performing model. Quantitative verification results suggest that the presently used convection and wet downburst forecasting techniques do not show much operational promise. As such, it is not recommended that the 45 WS use vertical profiles of thetae, wind speed, or wind direction to make specific predictions for which days are likely to produce convection or warning threshold wind gusts. None of the wet downburst indices used displayed much potential either. Although, the linear regression based predictive analytic models do not perform too well, CART based models perform better, especially those that utilize a binary response variable. Of the new techniques, the ensemble CART models displayed the most promise with the boosting algorithm showing nearly perfect results for predicting which days would produce convection and which days would produce warning threshold winds should convection be predicted.

  3. Sensitivity analysis of nacelle lidar free stream wind speed measurements to wind-induction reconstruction model and lidar range configuration

    DEFF Research Database (Denmark)

    Svensson, Elin; Borraccino, Antoine; Meyer Forsting, Alexander Raul

    configurations. The wind speeds were reconstructed using both a onedimensional and two-dimensional induction model to test the sensitivity towards the wind-induction model. In all cases, the sensitivity of the reconstructed wind speed was determined from the wind speed error and root mean square error (RMSE...... based on the NKE sensitivity analysis results. Based on these results, it is recommended to configure nacelle lidars to measure at approximately 3-5 ranges. The minimum distance should be configured to roughly 0.5 rotor diameters (Drot) while it is recommended that the maximum range lay within 1-1.5Drot...

  4. Evaluating Atlantic tropical cyclone track error distributions for use in probabilistic forecasts of wind distribution

    OpenAIRE

    Neese, Jay M.

    2010-01-01

    Approved for public release; distribution is unlimited This thesis investigates whether the National Hurricane Center (NHC) operational product for producing probabilistic forecasts of tropical cyclone (TC) wind distributions could be further improved by examining the distributions of track errors it draws upon to calculate probabilities. The track spread/skill relationship for several global ensemble prediction system forecasts is examined as a condition for a description of a full p...

  5. Power system integration and control of variable speed wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Eek, Jarle

    2009-12-15

    A wind power plant is a highly dynamic system that dependent on the type of technology requires a number of automatic control loops. This research deals with modelling, control and analysis related to power system integration of variable speed, pitch controlled wind turbines. All turbine components have been modelled and implemented in the power system simulation program SIMPOW, and a description of the modelling approach for each component is given. The level of model detail relates to the classical modelling of power system components for power system stability studies, where low frequency oscillations are of special importance. The wind turbine model includes a simplified representation of the developed rotor torque and the thrust force based on C{sub p-} and C{sub t} characteristic curves. The mechanical system model represents the fundamental torsional mode and the first mode of blades and tower movements. Two generator technologies have been investigated. The doubly fed induction generator (DFIG) and the stator converter interfaced permanent magnet synchronous generator (PMSG). A simplified model of a 2 level voltage source converter is used for both machine types. The generator converter controllers have been given special attention. All model components are linearized for the purpose of control system design and power system interaction related to small signal stability analysis. Different control strategies discussed in the literature have been investigated with regard to power system interaction aspects. All control parameters are identified using the internal model control approach. The analysis is focused on three main areas: 1. Identification of low damped oscillatory modes. This is carried out by the establishment and discussion of wind turbine modelling. 2. Interaction between control loops. A systematic approach is presented in order to analyse the influence of control loops used in variable speed wind turbines. 3.Impact on power system performance

  6. Flight speed and performance of the wandering albatross with respect to wind.

    Science.gov (United States)

    Richardson, Philip L; Wakefield, Ewan D; Phillips, Richard A

    2018-01-01

    Albatrosses and other large seabirds use dynamic soaring to gain sufficient energy from the wind to travel large distances rapidly and with little apparent effort. The recent development of miniature bird-borne tracking devices now makes it possible to explore the physical and biological implications of this means of locomotion in detail. Here we use GPS tracking and concurrent reanalyzed wind speed data to model the flight performance of wandering albatrosses Diomedea exulans soaring over the Southern Ocean. We investigate the extent to which flight speed and performance of albatrosses is facilitated or constrained by wind conditions encountered during foraging trips. We derived simple equations to model observed albatross ground speed as a function of wind speed and relative wind direction. Ground speeds of the tracked birds in the along-wind direction varied primarily by wind-induced leeway, which averaged 0.51 (± 0.02) times the wind speed at a reference height of 5 m. By subtracting leeway velocity from ground velocity, we were able to estimate airspeed (the magnitude of the bird's velocity through the air). As wind speeds increased from 3 to 18 m/s, the airspeed of wandering albatrosses flying in an across-wind direction increased by 0.42 (± 0.04) times the wind speed (i.e. ~ 6 m/s). At low wind speeds, tracked birds increased their airspeed in upwind flight relative to that in downwind flight. At higher wind speeds they apparently limited their airspeeds to a maximum of around 20 m/s, probably to keep the forces on their wings in dynamic soaring well within tolerable limits. Upwind airspeeds were nearly constant and downwind leeway increased with wind speed. Birds therefore achieved their fastest upwind ground speeds (~ 9 m/s) at low wind speeds (~ 3 m/s). This study provides insights into which flight strategies are optimal for dynamic soaring. Our results are consistent with the prediction that the optimal range speed of albatrosses is higher

  7. Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

    Directory of Open Access Journals (Sweden)

    Haoran Zhao

    2018-03-01

    Full Text Available As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N decomposition approach, the Least Square Support Vector Machine (LSSVM, and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA. For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM, and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM, it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

  8. A new approach to very short term wind speed prediction using k-nearest neighbor classification

    International Nuclear Information System (INIS)

    Yesilbudak, Mehmet; Sagiroglu, Seref; Colak, Ilhami

    2013-01-01

    Highlights: ► Wind speed parameter was predicted in an n-tupled inputs using k-NN classification. ► The effects of input parameters, nearest neighbors and distance metrics were analyzed. ► Many useful and reasonable inferences were uncovered using the developed model. - Abstract: Wind energy is an inexhaustible energy source and wind power production has been growing rapidly in recent years. However, wind power has a non-schedulable nature due to wind speed variations. Hence, wind speed prediction is an indispensable requirement for power system operators. This paper predicts wind speed parameter in an n-tupled inputs using k-nearest neighbor (k-NN) classification and analyzes the effects of input parameters, nearest neighbors and distance metrics on wind speed prediction. The k-NN classification model was developed using the object oriented programming techniques and includes Manhattan and Minkowski distance metrics except from Euclidean distance metric on the contrary of literature. The k-NN classification model which uses wind direction, air temperature, atmospheric pressure and relative humidity parameters in a 4-tupled space achieved the best wind speed prediction for k = 5 in the Manhattan distance metric. Differently, the k-NN classification model which uses wind direction, air temperature and atmospheric pressure parameters in a 3-tupled inputs gave the worst wind speed prediction for k = 1 in the Minkowski distance metric

  9. Examination of the wind speed limit function in the Rothermel surface fire spread model

    Science.gov (United States)

    Patricia L. Andrews; Miguel G. Cruz; Richard C. Rothermel

    2013-01-01

    The Rothermel surface fire spread model includes a wind speed limit, above which predicted rate of spread is constant. Complete derivation of the wind limit as a function of reaction intensity is given, along with an alternate result based on a changed assumption. Evidence indicates that both the original and the revised wind limits are too restrictive. Wind limit is...

  10. Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

    DEFF Research Database (Denmark)

    Dahl, Christian M.; Croonenbroeck, Carsten

    2014-01-01

    We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium......-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more...... accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology....

  11. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

    2016-04-01

    This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  12. Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

    Science.gov (United States)

    Henley, E. M.; Pope, E. C. D.

    2017-12-01

    This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

  13. The influence of wind speed on surface layer stability and turbulent ...

    Indian Academy of Sciences (India)

    wind regime (Mahrt et al. ... Influence of wind speed on surface layer stability and turbulent fluxes. 1401. Table 1. Specifications of the eddy ..... different soil and vegetation properties and other regional climatic factors. Earlier, it was found that.

  14. Flicker Mitigation by Individual Pitch Control of Variable Speed Wind Turbines With DFIG

    DEFF Research Database (Denmark)

    Zhang, Yunqian; Chen, Zhe; Hu, Weihao

    2014-01-01

    Due to the wind speed variation, wind shear and tower shadow effects, grid connected wind turbines are the sources of power fluctuations which may produce flicker during continuous operation. This paper presents a model of an MW-level variable-speed wind turbine with a doubly fed induction...... generatorto investigate the flicker emission and mitigation issues. An individual pitch control (IPC) strategy is proposed to reduce the flicker emission at different wind speed conditions. The IPC scheme is proposed and the individual pitch controller is designed according to the generator active power...... and the azimuth angle of the wind turbine. The simulations are performed on the NREL (National Renewable Energy Laboratory) 1.5-MW upwind reference wind turbine model. Simulation results show that damping the generator active power by IPC is an effective means for flicker mitigation of variable speed wind...

  15. Modelling and control of variable speed wind turbines for power system studies

    DEFF Research Database (Denmark)

    Michalke, Gabriele; Hansen, Anca Daniela

    2010-01-01

    and implemented in the power system simulation tool DIgSILENT. Important issues like the fault ride-through and grid support capabilities of these wind turbine concepts are addressed. The paper reveals that advanced control of variable speed wind turbines can improve power system stability. Finally......, it will be shown in the paper that wind parks consisting of variable speed wind turbines can help nearby connected fixed speed wind turbines to ride-through grid faults. Copyright © 2009 John Wiley & Sons, Ltd.......Modern wind turbines are predominantly variable speed wind turbines with power electronic interface. Emphasis in this paper is therefore on the modelling and control issues of these wind turbine concepts and especially on their impact on the power system. The models and control are developed...

  16. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    Directory of Open Access Journals (Sweden)

    Saber Talari

    2017-11-01

    Full Text Available Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA method and Radial Basis Function Neural Network (RBFN. To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

  17. Challenges, problems and possible solutions in wind generator systems from the aspect of forecast, planning and delivery of wind energy

    International Nuclear Information System (INIS)

    Giovski, Nikola

    2014-01-01

    The fundamental difficulties of integrating wind energy into the power system arise from its large temporal variability and limited predictability. That's why the integration of wind power presents major challenge for today's operating and planning practices of the power system operators. Accurate predictions of the possible wind power output, in time intervals relevant for creating schedules for production and exchange capacity, allows to system operators and dispatching personnel more efficient power system management. Despite the challenges and problems that arise due to integration of wind power into power systems, which need to be solved or reduced, wind power has its advantages that should be utilized. The effective integration of wind power plants into the transmission grid should allow them to represent the backbone of future energy systems. Modern wind generators represent production units that have the ability to participate in the management of energy systems e.g. in the regulation of frequency, voltage and other network operating requirements. This paper provides a brief overview of global experiences with the challenges, problems and possible solutions that appear in wind generator systems from the aspect of forecasting, planning and delivery of wind energy. (author)

  18. Low Wind Speed Turbine Developments in Convoloid Gearing: Final Technical Report, June 2005 - October 2008

    Energy Technology Data Exchange (ETDEWEB)

    Genesis Partners LP

    2010-08-01

    This report presents the results of a study conducted by Genesis Partners LP as part of the United States Department of Energy Wind Energy Research Program to develop wind technology that will enable wind systems to compete in regions having low wind speeds. The purpose of the program is to reduce the cost of electricity from large wind systems in areas having Class 4 winds to 3 cents per kWh for onshore systems or 5 cents per kWh for offshore systems. This work builds upon previous activities under the WindPACT project, the Next Generation Turbine project, and Phase I of the Low Wind Speed Turbine (LWST) project. This project is concerned with the development of more cost-effective gearing for speed increasers for wind turbines.

  19. Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea

    Directory of Open Access Journals (Sweden)

    U. Löptien

    2014-12-01

    Full Text Available The Baltic Sea is a seasonally ice-covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several ice properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS, with the respective forecasted