Method for identifying abnormity of state parameters of wind turbine generator based on fuzzy comprehensive evaluation

Abstract

The invention relates to a method for identifying abnormity of state parameters of a wind turbine generator based on fuzzy comprehensive evaluation. The method comprises the following steps: S1, obtaining the mean absolute error of a test sample according to a selection result of a state parameter generalized fuzzy abnormity identification model so as to obtain weights of various prediction models; S2, realizing state parameter prediction in a time interval to be analyzed through the various prediction models; S3, realizing condition analysis of state parameters through the residual error of the various predication models so as to obtain residual error abnormity indexes of various models; S4, calculating fuzzy membership degrees of various indexes to form a fuzzy evaluation matrix, and calculating an output layer evaluation value; and S5, inputting an evaluation result according to the membership degree maximum principle, and taking a corresponding comment as the evaluation result. The method disclosed by the invention is based on SCADA data of a wind power plant; the method is easy in programming realization; abnormity of parameters can be reflected accurately and effectively; and the identification accuracy of abnormity of the state parameters can be increased by comprehensively considering the abnormity identification results of the plurality of prediction models.

Description

technical field [0001] The invention belongs to the technical field of safety evaluation of new energy electric equipment, and relates to a method for identifying abnormal state parameters of a wind turbine based on fuzzy comprehensive evaluation. Background technique [0002] The condition monitoring parameters of the unit in the wind farm SCADA (Supervisory Control And Data Acquisition) system not only reflect the working status of the equipment, but also contain relevant information about the health status of the unit. The identification of abnormal operating conditions of unit state parameters based on SCADA system is an important way to obtain reliability information of wind turbines. However, the state parameters in the SCADA system are easily affected by wind speed and ambient temperature, and the abnormal information of the unit is easily concealed. [0003] At present, the main method for abnormal identification of the state parameters of the wind turbine is to est...

AI Technical Summary

Problems solved by technology

The prediction model based on historical data as training samples will affect the accuracy of the model due to equipment aging and maintenance. The prediction model based on recent data as training samples will have a greater impact on the sensitivity of abnormal identification due to the abnormal operation data.

Images