Classification of Faults in a Hybrid Power System using Artificial Neural Network

Electrical power transmission lines are characterized by very lengthy transmission lines and thus are more exposed to the environment. Consequently, transmission lines are more prone to faults, which hinder the continuity of electric power supplied, increases the loss of electric power generated and loss of economy. Quick detection and classification of a fault hastens its clearance and reduces system downtime thus, improving the security and efficiency of the network. Thus, this paper focuses on developing an artificial neural network to classify the faults in hybrid power system. This work employs a feed-forward artificial neural network with back propagation algorithm for fault classification. The instantaneous voltages and currents values are extracted and used to train the neural network. Simulation results have been provided to demonstrate the efficiency of the developed intelligent system for fault classification in grid-connected hybrid power system. The performance of the classifier is evaluated using the Mean Square Error (MSE).