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

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.