WIND POWER DATA CLEANING METHOD BASED ON RULE BASE AND PRRL MODEL
In order to improve the identification accuracy of abnormal data in the original data of wind farms,a wind farm data cleaning method combining a rule base and the PRRL model is proposed.First,a rule base is established based on parameters such as the installed capacity of the wind farm to increase the proportion of normal data in the dataset.Second,the PRRL robust regression model is constructed using the RANSAC robust regression algorithm as the core and the linear regression model as the foundation,taking wind speed data as the output and expanding the polynomial features of input variables to capture the nonlinear relationship between wind speed and power.The model is trained on data processed by the rule base,effectively identifying abnormal data in the original dataset.Application testing using sample data from a wind farm in Hunan shows that this method can effectively identify abnormal data when processing the data with a high proportion of abnormal data,and reduce the prediction error of the wind power forecasting model.
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