Online Cleaning of Abnormal Data for the Prediction of Wind Turbine Health Condition
Ma Ran1,2, Li Wenyi1,2, Qi Yongsheng2
1. College of Energy and Power Engineering Inner Mongolia University of Technology Hohhot 010050 China; 2. College of Electrical Engineering Inner Mongolia University of Technology Hohhot 010080 China
Abstract：Wind turbine (WT) supervisory control and data acquisition (SCADA) data contains a large number of abnormal data, which has a serious impact on the prediction of WT health condition. Therefore, an online cleaning method for abnormal data is proposed according to the measured wind-power and rotate speed-power data. Due to the complexity of data features in the process of WT performance degradation, key characteristic parameters are selected as data cleaning objects based on empirical Copula-based mutual information (ECMI), and the nonlinearity and uncertainty are described by establishing confidence equivalent power interval calculated with Copula. Accordingly, the Copula-based data cleaning model combining the time-series features and density distribution (Copula-TFDD) of abnormal points is established, and online cleaning for the stacking points and outliers outside the confidence boundary is performed in turn. Finally, through the actual data and the simulation data, the accuracy and efficiency of Copula-TFDD are analyzed, and the influence on the prediction of WT health condition is also analyzed. The results show that Copula-TFDD can accurately and real-time identify various abnormal data, effectively improving the prediction performance of WT health condition.
马然, 栗文义, 齐咏生. 风电机组健康状态预测中异常数据在线清洗[J]. 电工技术学报, 2021, 36(10): 2127-2139.
Ma Ran, Li Wenyi, Qi Yongsheng. Online Cleaning of Abnormal Data for the Prediction of Wind Turbine Health Condition. Transactions of China Electrotechnical Society, 2021, 36(10): 2127-2139.
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2021, Vol. 36 
