Fault early warning of wind turbine gearbox based on multi‐input support vector regression and improved ant lion optimization

• Main Authors: Yanjun Yang, Aimin Liu, Hongwei Xin, Jianguo Wang
• Format: Article
• Language: English
• Published: Wiley 2021-08-01
• Series: Wind Energy
• Subjects: fault early warning; prediction; support vector regression; trend analysis; wind turbine gearbox
• Online Access: https://doi.org/10.1002/we.2604

Abstract Gearbox oil temperature is one of the important indicators for gearbox condition monitoring and faults early warning. Accurately predicting the gearbox oil temperature change trend can maintain the gearbox in advance and ensure the safety and reliability of the wind turbine gearbox. The purpose of this article is to analyze the supervisory control and data acquisition (SCADA) data in wind turbines. A method based on multi‐input improved ant lion optimization and support vector regression (M‐IALO‐SVR) proposed, which can accurately predict the gearbox oil temperature. The prediction method is compared with back propagation neural network (BPNN) and ALO‐SVR methods to verify the effectiveness of the M‐IALO‐SVR method. To further analyze the prediction results, the 95% confidence interval processing is performed on the residuals of the prediction model, and then the trends of the mean and standard deviation of the moving window residuals are calculated. Testing SCADA data from a wind farm in northeast China, the test results show that when the gearbox is operating normally, the predicted value of the gearbox oil temperature follows the measured value very well. When the gearbox operates abnormally, its temperature deviates from the normal range, and the statistical characteristics of the residuals also change. According to the trend of the residuals statistical characteristics, the abnormal state of the gearbox can be found in time.