High availability of wind power data is the basis for wind power research

but there are a large number of abnormal data in actual collected data

which seriously affects analysis of wind power law and reduces prediction accuracy. Measured power data of wind farm are analyzed

influence of wind speed fluctuation characteristics on wind power is discussed

and abnormal points are identified for data of different wind types. The Cluster-Based Local Outlier Factor (CLOF) algorithm based on K-means is used to identify outlier abnormal points

and conditional constraints based on physical background are used to identify accumulation abnormal points. Reconstructed data segment is divided according to fluctuation of wind speed. The Bidirectional Gate Recurrent Unit (BiGRU) model with wind speed as input reconstructs fluctuation segment data

and bi-directional weighted random forest model reconstructs stationary segment data. Based on analysis of measured data of a wind farm

results show the method can effectively identify various abnormal data

and complete high-quality reconstruction of data

thereby improving accuracy of wind power prediction.