Fault diagnosis of substation power's inner line based on Optimum-Path forest algorithm

Fengxiang Ni, Yufu Guo, Cunlong Zheng, D. Huang, Haoliang Sun, Y. Zhou
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Abstract

For the problems of large cost of manual troubleshooting and insufficient detail of operation and maintenance management for fault diagnosis in substation power's inner lines, the fault classification task is implemented for line faults in substations based on Optimum-Path Forest (OPF) algorithm using Python and Scikit-learn platform, and the classification accuracy of the algorithm is experimented on public fault dataset. The algorithm achieves a classification accuracy of 95.07%, which is better than traditional methods such as plain Bayes, random forest, and SVM. The experiments show that the algorithm can reduce the difficulty of the operation and inspection personnel to carry out detection, enhance the reliability of the substation line protection system, and provide a reliable power supply for the smart substation.
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基于最优路径森林算法的变电站内线故障诊断
针对变电站电力内线故障诊断中人工故障排除成本大、运维管理不够详细等问题,利用Python和Scikit-learn平台,实现了基于OPF算法的变电站线路故障分类任务,并在公共故障数据集上对算法的分类准确率进行了实验。该算法的分类准确率达到95.07%,优于朴素贝叶斯、随机森林、SVM等传统方法。实验表明,该算法可以降低操作人员和巡检人员进行检测的难度,增强变电站线路保护系统的可靠性,为智能变电站提供可靠的供电。
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