A trend cloud model-based approach for the identification of wind turbine gearbox anomalies.

This paper deals with the identification of anomalies in wind turbine (WT) gearbox by temperature trend analysis approach. Support vector regression (SVR) is adopted to build two models for forecasting operating temperature of WT gearbox. One model is trained with historical supervisory control and data acquisitions (SCADA) data in the normal state, and the other is trained with abnormal state data. The prediction accuracy of two models is compared, and the sequences of relative error (SRE) index for two models are calculated. Then, two trend cloud model, namely normal cloud, and abnormal cloud, are built based on an improved inverse normal cloud generator, meanwhile the SRE are used as inputs of the generator, and the parameters of different trend cloud models are obtained as outputs. The closeness degree of the current state related to the normal or abnormal cloud can be calculated using the current SCADA data, and the principle of maximum closeness degree is adopted to judge the anomaly. The proposed approach has been used to analyze a real gearbox failure occurred in a 1.5MW WT. The results obtained confirm the feasibility and efficiency of the proposed approach.