Wind power ramp event forecasting based on feature extraction and deep learning

Abstract

In order to improve the accuracy of wind power ramp forecasting and reduce the threat of ramps to the safe operation of power systems, a wind power ramp event forecast model based on feature extraction and deep learning is proposed in this work. Firstly, the Optimized Swinging Door Algorithm (OpSDA) is introduced to detect wind power ramp events, and the extraction results of ramp features, such as the ramp rate, are obtained. Then, a ramp forecast model based on a deep learning network is established. The historical wind power and its ramp features are used as the input of the forecast model, thereby strengthening the model’s learning for ramp features and preventing ramp features from being submerged in the complex wind power signal. A Convolutional Neural Network (CNN) is adopted to extract features from model inputs to obtain the coupling relationship between wind power and ramp features, and Long Short-Term Memory (LSTM) is utilized to learn the time-series relationship of the data. The forecast wind power is used as the output of the model, based on which the ramp forecast result is obtained after the ramp detection. Finally, the wind power data from the Elia website is used to verify the forecast performance of the proposed method for wind power ramp events.