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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 1.5 MW WT. The results obtained confirm the feasibility and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

To satisfy the requirements of on-line status assessment of wind turbines, a fuzzy comprehensive evaluation method combined with normal cloud model based on SCADA data was proposed. Firstly, a concept was introduced, The Dynamic Inferior, which can take both the variation of assessment indices into the calculation process of inferior degree and the changing of the wind turbine operating condition into consideration. A Markov chain model was adapted to predict the variation of assessment indices. Then, a normal cloud model with the dynamic inferior degree as inputs was adopted to calculate the membership degree of different assessment indices to overcome the subjectivity of normal membership functions. Furthermore, a fuzzy comprehensive assessment method was developed to assess the status of wind turbines online. Finally, the method was tested on the historical SCADA data of wind turbines. The results showed that this method was capable of not only accurately assessing the status of wind turbines online, but could also warn of problems early, which can prevent the occurrence of serious complications. [ABSTRACT FROM AUTHOR]