An Operating Condition Recognition Method of Wind Turbine Based on SCADA Parameter Relations

Abstract

An operating condition recognition method of wind turbine is proposed based on the parameter relations of SCADA data. Starting from the operation characteristics of wind turbine, the SCADA data input/output parameters relations of wind turbine operating condition are analyzed in-depth. By using a time sliding window model, the polynomial regression fitting method is adopted to build mathematic model of input/output parameters of wind turbine operating status. Then, based on the mathematic model of input/output parameters of wind turbine normal operation, an index formula for describing the abnormality of wind turbine operating status at each moment is put forward. The statistical analysis of the wind turbine state index is carried out in normal operation phase to obtain the distribution function. Finally, according to the hypothesis of small probability event, the threshold value of the state index of the wind turbine abnormal state is determined and the operation condition of wind turbine is forewarned accordingly. Taking the SCADA data of two 2 MW direct-driven wind turbines of the same wind farm as an example, the results show that: ① The proposed wind turbine operating condition recognition method that based on SCADA data can realize the abnormal status identification and early warning of wind turbine operation condition. The feature of this method is that the condition recognition is entirely based on normal operation SCADA data analysis which means mining training and prior knowledge of related physical mechanism and failure mode of abnormal operation SCADA data will not be required. ② Based on the proposed operating condition recognition method, according to the hierarchical structure of the relationship between input/output parameters of wind turbine and its components, the relevant parts information of the abnormal state can be obtained, which is of great significance for operating status early warning and maintenance decision-making of wind turbine.