A fault detection framework using recurrent neural networks for condition monitoring of wind turbines

Abstract

This paper proposes a fault detection framework for the condition monitoring of wind turbines. The framework models and analyzes the data in supervisory control and data acquisition systems. For log information, each event is mapped to an assembly based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed to determine the current operation status. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate the effect of minor fluctuations. The generated results from the framework can help to understand when the turbine deviates from normal operations. The framework is validated with the data from an onshore wind park. The numerical results show that the framework can detect operational risks and reduce false alarms.