Power System Fault Detection, Classification and Location using Artificial Neural Networks

This article focuses on detecting, classifying and locating faults in power system using Artificial Neural Networks (ANNs). Feed-forward neural networks have been employed and trained with back-propagation algorithm. The model of WSCC 9 bus test system has been modelled in Matlab/Simulink, and used to validate the proposed fault detection system. First, normal state of the model was observed. After determination of normal state, different types of faults have been simulated on all nine buses and on all lines of the model. Voltage and current magnitudes, obtained by the fault simulation, are used as inputs of the ANN. Output of the ANN should provide information about the fault type and location in case the fault occurs. Number of hidden layers and neurons per hidden layer is determined by testing performance and training time of each developed neural network.