Abstract
A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.
Highlights
The sense of crisis brought about by the depletion of fossil energy has prompted the global energy revolution [1,2]
The results show that the proposed hybrid model can produce a more accurate power forecast at a given wind speed, resulting in a better power curve
In order to test the performances of different models, four error indicators, mean absolute error (MAE), root mean square error (RMSE), normalized mean absolute percentage error (NMAPE) and the coefficient of determination (R2 ), were employed
Summary
The sense of crisis brought about by the depletion of fossil energy has prompted the global energy revolution [1,2]. Multilayer perceptron, and general regression neural network,towere and their performances many artificial intelligence-based models have been employed learnused, the above complex nonlinear were compared power curve modeling. Proposed spline regression models to obtain accurate power curvesspline in different wind models, farms and seasons two Bayesian-based models, heteroscedastic and robust regression to different fit deterministic and probabilistic power curves in different seasons, respectively. From the above literature review, it can be found that only one power curve model is selected and used to fit the real WTPC with the measured wind data. It is reported that there are many outliers in the collected wind data [32,33,34] They have adverse effects on the learning process of power curve models and prevent us from obtaining accurate WTPCs [34].
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