Tree Based Fault Classification in Underground Cable

This paper presents tree-based machine learning techniques like decision trees, and random forest algorithms to categorize various permanent faults such as a line to ground, line to line, line-line to ground, and line-line-line to ground fault simulated in an underground cable. The simulation has been performed with the variation in fault types, fault location, fault resistance, and fault inception angle and the sending end current signals are presented as input to the classifier for classifying the fault. A relative comparison between the decision tree technique and random forest technique has been performed and the random forest algorithm turns out to be the best for classifying the permanent faults in an underground cable. Both the algorithms are deliberated by precision, F1 score, and recall. The best result is presented by Random Forest Algorithm with an accuracy of 98.8%.