Short-Term Reliability Prediction of Key Components of Wind Turbine Based on SCADA Data

Abstract

In this paper, the Principal Component Analysis (PCA) method combined with the Radial Basis Function (RBF) neural network is used to establish a short-term reliability prediction model for wind turbines based on the SCADA data. The PCA method is used to reduce the dimensionality of the SCADA data and extract the principal components as the input data of the RBF neural network. The RBF neural network is used to predict the running state of key components of the wind turbine. Finally, a short-term reliability prediction model of wind turbines based on PCA-RBF is established. With the real wind farm SCADA data, the short-term reliability of wind turbine gearbox is predicted. The result shows that the short-term reliability prediction model can better reflect the reliability of key components and provide reference for the operation and maintenance of wind turbines.