Since large amount of abnormal data exists in wind power operation data

this paper proposes a wind power data cleaning method based on the confidence equivalent boundary model

which combines operation process of wind turbines and statistical uncertainty of operation data. Firstly

according to the wind turbine operation mechanism and strategy

the three-dimension relationship among wind speed

rotor speed and output power is extracted. Using the relationship

segmentally refined culling to the abnormal data in accordance with wind speed can be executed. On this basis

the wind power scatter plot is divided into different regions. The Copula theory is adopted to establish the conditional probability distribution of output power under the variable wind speed. Thus

the equivalent boundary model of output power with certain confidence level can be calculated. The model can be directly used to identify and cull the abnormal data and to improve the ratio of valid data. Then

the piecewise cubic Hermite interpolation method is adopted to reconstruct the tiny missing data

and the whole wind speed and power data can be obtained. Finally

the performance index such as the confidence bandwidth ratio is defined to evaluate the data cleaning quality. Furthermore

the k-fold cross validation method is applied in the checking of equivalent boundary model. In the example analysis

the operation data of different wind turbines with the same type is sampled. The results show that the cleaned data has higher confidence bandwidth ratio

more moderate skewness and greater kurtosis. It indicates that the ratio of valid data greatly increases and becomes more concentrative in distribution

which means that the proposed method is effective and reasonable.