王燕,蒋逸雯,李黎,魏东亮,薛峰,谢建容.多源异构数据融合的避雷器运行状态评价方法[J].电测与仪表,2020,57(19):132-139. WANG Yan,JIANG Yiwen,LI Lee,WEI Dongliang,XUE Feng,XIE Jianrong.Condition Assessment of Arresters Based on Multi-Source Heterogeneous Data Mining[J].Electrical Measurement & Instrumentation,2020,57(19):132-139.
多源异构数据融合的避雷器运行状态评价方法
Condition Assessment of Arresters Based on Multi-Source Heterogeneous Data Mining
Metal oxide arrester with electric charge runs for a long time, and the latent defects cannot be identified by regular maintenance or online monitoring. Therefore, this paper proposes a data mining method to assess the operating condition of the arrester with the typical operating parameters. First, the characteristic parameters from the lightning detection, online monitoring, on-site inspection, and pre-operation information of arresters are selected and formed a defect feature quantity database. Then, the semi-trapezoidal model is used to normalize the quantitative parameters, and the natural language processing technology is introduced to normalize the qualitative parameters. Besides, a data fusion method based on random forest optimization is proposed. Finally, all of the arrester data for a substation are adopted for analysis. The example shows that the assessment accuracy of the proposed model is 93.12%, and it has better generalization ability than the decision tree model and the support vector machine model.
