Fault detection of wind energy conversion systems using recurrent neural networks

Abstract

Structure of wind energy conversion systems (WECSs) must be robust against faults. In order to accurately study WECSs during occurrence of faults and to explore the impact of faults on each component of the WECSs, a detailed model is required in which both mechanical and electrical parts of the WECSs are properly involved. In addition, a fault detection system (FDS) is required to diagnose the occurred faults at the appropriate time in order to ensure a safe system operation, avoid heavy economic losses, prevent damage to adjacent relevant systems and facilitate timely repair of failed components. This can be performed by subsequent actions through fast and accurate detection of faults. In this paper, by utilising a comprehensive dynamic model of the WECS, an FDS is presented using dynamic recurrent neural networks. In industrial processes, dynamic neural networks are known as a good mathematical tool for fault detection. The proposed FDS detects faults of the generator's angular velocity sensor, pitch angle sensors and pitch actuators. The presented FDS has high capability of fault detection in short time and it has much low false alarms rate. Simulation results verify validity and usefulness of the proposed fault detection scheme.