Abnormal data cleaning of wind turbine power curve using Bayesian change point-quartile combined algorithm

Abstract

During the operation of wind turbines, there are a lot of abnormal data collected from wind power data, which cannot be used to truly evaluate the performance and operation state of wind turbines. According to the distribution characteristics of these abnormal data, an outlier delete method using Bayesian change point-quartile combined algorithm is proposed. Firstly, to identify and delete the abnormal data below wind power curve, based on the Bayesian theorem, change point position is identified. Finally, the quartile method is utilized to delete the upper sparse outliers. The proposed method is applied to 10-min field monitoring wind power data, root mean square error (RMSE), program running time and the rejection rate of field wind turbine data are used to measure model accuracy, the results show that compared with the least squares (LS) change point method, the method proposed in this paper is feasible and effective.