1.Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University of Ministry of Education, Jinan 250061, China;2.Jinan Power Supply Company of State Grid Shandong Electric Power Company, Jinan 250001, China

A method for improving the accuracy of anomaly detection results is proposed by using the supervisory control and data acquisition (SCADA) data of wind turbines with similar operation state in wind farms. Firstly, a similarity comparison principle of operation state for wind turbines is proposed based on the analysis results of actual data, and a similarity evaluation method of operation state is proposed based on the mutual information feature selection algorithm and the iterative self-organizing data analysis clustering algorithm. Secondly, considering the temporal dependence of state variables, a deterministic estimation model based on the support vector machine is constructed by the historical SCADA data of the wind turbines to be tested. A combined probability estimation model based on kernel density estimation is also built by the historical SCADA data of the wind turbines with similar operation state. Furthermore, the deterministic estimation model and the combined probability estimation model are used for self-inspection and external-inspection of the abnormal states of the target variables, respectively. The accuracy and reliability of anomaly detection results can be improved by verifying the two detection results. Finally, the feasibility and accuracy of the proposed method are verified based on the SCADA data of all wind turbines in a wind farm and comparative experiments.