A Fault and Capacity Loss Prediction Method of Wind Power Station under Extreme Weather

Abstract

Extreme weather events can severely affect the operation and power generation of wind farms and threaten the stability and safety of grids with high penetration of renewable energy. Therefore, it is crucial to forecast the failure and capacity loss of wind farms under extreme weather conditions. To this end, considering the disaster-causing mechanism of severe weather and the operational characteristics of wind farms, this paper first uses the density-based spatial clustering of applications with noise algorithm to cluster the units in the wind farm based on the operating characteristics affected by the weather, and uses correlation analysis methods to extract key disaster-causing factors in extreme weather; then proposes a prediction model based on feature-weighted stacking integration. The model adopts the stacking-integrated learning architecture to support multiple learners and performs feature weighting according to the prediction accuracy of each learner in the base learner, thereby improving the training effect of the meta-learner and improving the prediction accuracy of the model. The prediction model is used to predict each wind turbine group based on the extracted key features and to predict the failure and capacity loss of the wind farm. Finally, an example analysis is performed based on actual data from a wind farm, and the results show that the proposed prediction method can effectively predict the operational reliability of wind farms.