Fault Detection of Wind Turbine Pitch Motors Based on Ensemble Learning Approach

Abstract

Machine learning-based condition monitoring of wind turbines’ critical components is an active area of research, especially for pitch systems, which suffer from a high failure rate. In this work, we successfully predicted and detected the high-temperature fault of the electric pitch motor by analyzing SCADA data through the ensemble learning-based approach. For that, normal behavior models to predict pitch motor temperature were constructed respectively for three pitch motors by gradient boosting tree regression. Residual evolution before the reported high-temperature fault was studied by the sliding window approach. A Shewhart control chart was applied to detect the anomalies of temperature. The proposed approach successfully gave an early warning for the potential high-temperature fault of electric pitch motors around ten days prior to the SCADA system.