Day-ahead wind power forecasting based on the clustering of equivalent power curves

Wind power prediction (WPP) has developed in recent years into a way to solve the strong fluctuation problems that are caused by large-scale integration. Higher prediction accuracy is important to improve power grid security and economy. Wind turbine power curves, which describe the transformation between speed and power output, have been widely applied to WPP. In order to improve the accuracy of the prediction results and reduce the complexity of the model, this research proposes an improved Fuzzy C-means (FCM) Clustering Algorithm for day-ahead wind power prediction to resolve the difference in wind power output. By using the principle of minimum distance to select the relatively rough initial cluster centers of the samples, better clustering results can be obtained. The improved FCM method is used to classify turbines with similar power output characteristics into several categories, and a representative power curve is selected as the equivalent curve of the wind farm. And then capture the performance of the wind turbine. A day-ahead WPP model which utilizes numerical weather predictions (NWPs) as inputs for a subsequent equivalent power curve model is therefore established. The model proposed was validated using historical data taken from two different wind farms located in northeastern China. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

An improved Fuzzy C-means clustering algorithm is proposed to classify turbines with similar power output characteristics into several categories and select a representative power curve as the equivalent curve of the wind farm, aiming to improve the accuracy of wind power prediction and reduce model complexity.