A trend-based method for the prediction of offshore wind power ramp

Abstract

Wind power ramp is a destructive event accompanied with a sharp change of wind power. Offshore wind power is expected to receive more attention for it can harvest consistent and strong winds. In this paper, a ramp detection framework based on the swinging door algorithm (SDA) and the mergence of correct ramps is proposed. Meanwhile, a trend-based prediction method (T-Method) is proposed to predict offshore wind power ramps. The ramp detection framework is utilized to identify wind power ramp events (WPREs) and label original data according to the detection results. After that, the labeled data is selected as the input of recurrent neural network to produce wind power prediction results. Finally, the prediction results are detected by the ramp detection framework to produce WPREs prediction results. Four recurrent neural network models and two traditional methods are applied to two offshore wind power datasets for corroborating the effectiveness of our method. Comparative experiments show that our proposed method performs excellent in all evaluation indicators. The proposed WPREs prediction method improves the performance of WPREs prediction under the premise of low false alarm rate and missing alarm rate. It provides early warning for power system operators to reduce the harm of WPREs.