Wind turbine generator state parameter abnormity identification method based on combination prediction

Abstract

The invention relates to a wind turbine generator state parameter abnormity identification method based on combination prediction. The method comprises following steps of selecting proper wind power plant SCADA data to obtain training data and test data; establishing an individual prediction model including a BPNN and an LSSVM of a target parameter, optimizing the combination model and selecting proper weight; using a combination prediction model to predict the target parameter and combining the target parameter with an actual value so as to obtain a residual error; calculating a root mean square error (RMSE) and obtaining change conditions of the RMSE; if the RMSE is smaller than a threshold value, judging that the a state parameter is normal and if the RMSE is larger than the threshold value, using the same residual error data to calculating an entropy value; if the entropy value is smaller than the threshold value, judging that the state parameter is normal, wherein although the RMSE is larger than the threshold value at this moment, the residual data does not change too much, so it is impossible to judge that there is abnormity; and if the entropy value is larger than the threshold value, judging the state parameter is abnormal. According to the invention, programming is easy to achieve; and abnormity identification can be quickly and precisely carried out on wind turbine generator state parameters.

Description

technical field [0001] The invention belongs to the technical field of safety evaluation of new energy electric equipment, and relates to a combined prediction-based abnormal identification method for state parameters of wind turbines. Background technique [0002] Wind turbines are composed of mechanical, electrical and control components, etc. Any failure of any component may lead to the shutdown of the unit, and serious failures may even affect the safe and stable operation of the power system. The reliable operation of wind turbines is the basic guarantee for the safe and economical operation of wind farms. Installing a data acquisition and supervisory control (SCADA) system to monitor the real-time operation status of wind turbines in wind farms is a commonly used measure at present. The SCADA data of wind farms not only includes direct information such as status information, operation indication signals, and alarm signals of wind turbines, but also includes Indirect i...

AI Technical Summary

Problems solved by technology

[0004] The state parameters of wind turbines are comprehensively affected by factors such as the environment, power grid, and loads. The complex influence relationship between the components of the wind turbine and the state parameters makes it very difficult to identify abnormalities in the state parameters of wind turbines, and it is difficult to predict whether wind turbines will occur. abnormal

Various failure causes of wind turbines make the distribution characteristics of SCADA data complex, and the operation of the turbines is affected by various operating conditions. It is difficult to determine the abnormality of wind turbines by using algorithms such as classification and clustering

Therefore, the anomaly identification method based on statistics and machine learning is difficult to be used for anomaly identification of wind turbines

Images