Machine Learning Approach to Predict the Fault in Overhead Transmission Line

Fault prediction is one of the major parameter for the reliable operation of any stage of power system. In conventional analysis of power systems various faults like LG, LL, LLG, LLL, LLLG are the major faults that occurs in transmission and distribution system of the power systems. Even though there are several techniques to clear the fault in less clearance time, but the prediction favors the reliability of the system. In the advanced applications of machine learning (ML) algorithms, we can predict any type of fault with best accuracy. In this paper various ML algorithms were applied to predict different types of faults with best accuracy. Finally, experimental results prove that Decision tree algorithm gives highest accuracy than other algorithms like Logistic Regression, Random Forest, SVM, KNN, Naive Bayes, XG Boost & Light GBM.