Quantitative evaluation of wind turbine faults under variable operational conditions

Wind turbines have been widely used for clean and renewable electricity generation. The maintenance costs of wind turbines constitute a significant portion of the total cost of the generated electricity. Thus, health management systems are increasingly needed to reduce the maintenance costs and improve the reliability of wind turbines. This paper proposes a novel framework for quantitative evaluation of faults and health conditions of wind turbines using generator current signals. A synchronous resampling algorithm is designed to handle nonstationary current signals for fault feature extraction. The extracted fault features are used to reconstruct new signals, whose correlation dimensions are then calculated by using the Grassberger-Procaccia algorithm for fault and health condition evaluation of the wind turbines. Experimental studies are carried out for a wind turbine in the healthy condition and two faulty conditions. Results show that the proposed framework can not only detect the faults but also quantify different health conditions for the wind turbine.