Fault prognosis of wind turbine generator using SCADA data

Abstract

Accurate prognosis of wind turbine generator failures is essential for reducing operation and maintenance costs in wind farms. Existing methods rely on expensive, purpose-built condition monitoring systems to conduct diagnosis and prognosis of wind turbine generator failures. In this paper, we present a prognosis method to predict the remaining useful life (RUL) of generators, which requires no additional hardware support beyond widely adopted SCADA system. This work first introduces a notion, Anomaly Operation Index (AOI), to quantitatively measure wind turbine performance degradation in runtime. It then presents a data-driven wind turbine anomaly detection method and a time series analysis method to predict the wind turbine generator RUL. Experimental study on real-world wind farm data demonstrates that the proposed methods can achieve accurate prediction of wind turbine generator RUL and provide sufficient lead time for scheduling maintenance and repair.