Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis

Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines. Currently in the wind power industry, maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable, instead of being determined by the actual condition of the wind turbine. Among the total maintenance costs, approximately 25%~35% is related to regularly scheduled preventive maintenance and 65%~75% to unscheduled corrective maintenance. To reduce the failure rate and level and maintenance costs and improve the availability, reliability, safety, and lifespans of wind turbines, it is desirable to perform condition-based predictive maintenance for wind turbines, which will require a high-fidelity online prognostic condition monitoring system(CMS) for fault diagnosis and prognosis and remaining useful life(RUL) prediction of wind turbines. Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction. Compared to vibration monitoring, the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost, hardware complexity, implementation, and reliability. This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.