Multi-component Fault Detection in Wind Turbine Pitch Systems using Extended Park's Vector and Deep Autoencoder Feature Learning

摘要

Pitch systems are among the wind turbine components with most frequent failures. This article presents a multi-component fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions.