基于偏互信息法与支持向量机的覆冰闪络故障预警

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基于偏互信息法与支持向量机的覆冰闪络故障预警

更新时间：2025-08-13

- 基于偏互信息法与支持向量机的覆冰闪络故障预警
- Automation of Electric Power Systems Vol. 42, Issue 2, Pages: 92-98(2018)
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- Published：2018
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[J]. Automation of electric power systems, 2018, 42(2): 92-98.
DOI：

[J]. Automation of electric power systems, 2018, 42(2): 92-98. DOI：

摘要

输电线路覆冰闪络跳闸故障是引起电网故障的重要原因之一。现有的覆冰闪络研究主要集中在绝缘子覆冰闪络电压的模型研究。一方面，闪络电压模型不能全面反映所有因素综合作用下的绝缘子闪络电压；另一方面，数据采集的误差使现有覆冰闪络电压模型的研究成果难以在覆冰闪络故障预警中直接应用。考虑到数据挖掘技术的发展，基于偏互信息法和支持向量机对覆冰闪络故障进行预警。首先，采用偏互信息法筛选出关键的因素作为输入变量。然后，建立覆冰闪络预警的支持向量机模型，对样本数据进行训练和预测。仿真结果表明，基于偏互信息法与支持向量机的覆冰闪络故障预警方法能够较为有效地预测覆冰闪络，为实际电网的覆冰闪络防御提供了参考。

Abstract

Icing flashover fault of transmission line is one of important reasons leading to power grid failure. Current icing flashover researches mainly focus on the model study of flashover voltage of iced insulators. On one hand

the model of flashover voltage cannot fully reflect the insulator flashover voltage under the combined effects of all factors. On the other hand

due to the data acquisition error in the information of the transmission lines

the current characteristics model of icing flashover is difficult to be directly used in the forecasting of icing flashover fault. Considering the development of data mining technology

the partial mutual information(PMI)and support vector machine(SVM)are proposed to predict the icing flashover fault. Firstly

PMI is adopted to select the key factors for input variables. Secondly

SVM forecast model of icing flashover is established to train and predict the sample data. The simulation results show that the forecast method based on the PMI and SVM can predict the icing flashover more effectively

which can act as a reference for the ice defense of power grid.

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