Fault diagnosis of wind turbine pitch system based on LSTM with multi-channel attention mechanism

Abstract

Pitch system can adjust the blade’s pitch angle, control the aerodynamic torque and capture the wind turbine’s aerodynamic power. It is critical guarantee for the turbine’s safety and stability. However, the turbine’s operating environment is harsh and the long-term component abrasion may cause the system failure. Only by the monitoring signals’ instantaneous value, it is difficult to discovery the system fault. Therefore, this paper fuses multiple time sequence signals and uses the multi-channel attention mechanism to improve the long short-time memory networks (MCA-LSTM) for pitch system fault diagnosis. Firstly, the data of SCADA is preprocessed to construct the time sequence ratio vectors and input into MCA-LSTM. Through the attention mechanism, the time sequences ratio vectors are integrated from different channels in MCA-LSTM. Then the MCA-LSTM establishes the logical relationship between the signals and the fault features. Finally, root-mean-square error and the residual fluctuation threshold are used to identify the abnormal states. The experiments utilized the SCADA data of wind farms. The results show that MCA-LSTM can detect the pitch bearing anomalies and hub faults, which are about ten hours earlier than the wind farm fault recording. So, MCA-LSTM can effectively predict pitch system faults and provides an important basis for the WT fault diagnosis.