Hybrid Outliers Identification of High-dimensional Wind Power Operation Data

Aiming at a large number of abnormal data in wind turbine operation data, considering the wind turbine operation characteristics and data clustering, this paper proposes a method for cleaning abnormal power data of wind turbines based on high-dimensional space clustering. First, based on the control strategy differences and operating mechanisms of wind turbines, the mechanism cleaning of abnormal wind power data is performed; Second, consider the wind speed, rotor speed, and power, and use Gaussian mixture model (GMM) to cluster the data in three-dimensional space to achieve the preliminary cleaning of abnormal power data of wind turbines; Finally, on the basis of the preliminary cleaned data, an optimized multi-directional quartile method is used in the Copula space for refined cleaning. The actual data of a certain type of wind turbine in North China is selected for example analysis. The simulation results show that the method proposed in this paper is reasonable and effective.