关键词: 风电机组, 性能评估, 信息融合, 深度信念网络, 自组织映射

Abstract: To solve the problem that single-index normal behavior models of wind turbines might give false alarms,a new performance evaluation method based on information fusion for wind turbines was proposed.The Local Outlier Factor(LOF)algorithm was improved for screening normal operating data.Afterwards Kendall correlation coefficient was applied to parameter selection and several normal behavior models were constructed with Deep Belief Network(DBN),where the residuals between actual operating data and model prediction value represented performance features.Information fusion was then realized by mapping the residual space to operating behavior space using Self-organizing Mapping(SOM)neural network.On this basis,a performance index based on the deterioration degree of operating states was calculated for evaluating the performance of wind turbine.The validity and superiority of the proposed method was illustrated using real-world wind turbine operating data.

Key words: wind turbine, performance assessment, information fusion, deep belief network, self-organizing map