Fault Prediction Model of Wind Power Pitch System Based on BP Neural Network

Pitch system fault prediction and improvement of prediction accuracy are key technologies for wind power development, which ensure safe operation of the grid and effectively reduces operation and maintenance costs. The Supervisory control and data acquisition (SCADA) system data is analyzed and processed to extract the associated parameters, i.e. output power, wind speed, pitch angle, and rotor speed. A Back Propagation (BP) neural network is used to train the system, taking into account the volatility and uncertainty of wind turbine parameters, and a regression prediction model with a support vector regression (SVR) algorithm is also used for training. A pitch failure prediction model is established to predict the operation of the pitch system, which is used to develop a reasonable operation and maintenance plan. Through the system simulation, the prediction model performance index, error-index, and output data graphics are compared and analyzed.