Abstract：An abnormal state early warning method for wind generating units is proposed based on support vector data description(SVDD) and XGBoost（eXtreme gradient boosting）model. The feature variables closely related to the generator speed and output power are selected. Then SVDD algorithm is employed to preprocess the SCADA historical data and the XGBoost-based normal performance prediction model is set up. The time-sliding window model is constructed to calculate the performance evaluation index on the basis of the developed model, and the threshold value of which is determined in accordance with the interval estimation theory of statistics. The abnormal state warning tests are carried out using several true historical fault cases recorded in the SCADA system of a 1.5MW wind power unit. It is shown that the abnormal state warning method based on SVDD and XGBoost can clean the original data effectively, and identify the wind turbine abnormal state timely. The proposed method has practical engineering significance for improving the operation safety of wind turbine generator system.
马良玉, 程善珍. 基于支持向量数据描述和XGBoost的风电机组异常工况预警研究[J]. 电工技术学报, 2022, 37(13): 3241-3249.
Ma Liangyu, Cheng Shanzhen. Abnormal State Early Warning of Wind Turbine Generator Based on Support Vector Data Description and XGBoost. Transactions of China Electrotechnical Society, 2022, 37(13): 3241-3249.
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