The use of machine learning techniques to classify power transmission line fault types and locations

Transmission lines are very important component of the electric power system. Therefore it is necessary to predict and detect transmission lines fault types and locations to enhance the power system protection scheme and increase its reliability. This paper investigates the use of four powerful machine learning classifiers to detect and predict fault types and locations over a 750KV, 600km long power transmission line. Bagging, Boosting, radial basis functions and naïve Bayesian classifiers were utilized for locating and detecting faults in a power transmission line. Findings exhibits that using machine learning technique could be feasible for such task and may represent a great opportunity to increase the power system protection and efficiency.