POWER SYSTEM RESTORATION USING ARTIFICIAL NEURAL NETWORKS

Abstract

This thesis focuses on power system restoration using artificial neural networks stream-lined to just transmission lines by detecting, classifying and locating faults on electric power transmission lines. Feed-forward networks have been employed along with back-propagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulations were done in MATLAB7.5 software and results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances compared to other types of techniques used in detecting faults on electric power transmission lines. The simulated result for fault detection found the best artificial neural network configuration of (610-5-3-1) which gave98% result after training the network and the result for fault classification found the best artificial neural network configuration of (6-35-4) which gave 97% result after training the network. While the result for fault location dealt with the design, development and the implementation of the neural network based fault locators for each of the various types of faults which are line to ground, double line to ground, line to line and three phase faults (L-G, LL-G, LL and 3phase) and in these different types of faults its results are based on the appropriate mean square error for each simulation. The result obtained for single line to ground fault location which is seen to be satisfactory at the end of the training and testing process of the artificial neural network is said to have the network configuration of 6-input neurons, 7- hidden neurons, 1-output neuron (6-7-1), with an average error of 0.89% and can be used for the purpose of single line to ground fault location. For line to line fault, it is seen that the network configuration of 6-input neurons,10-hidden neurons,5-hidden neurons,1-output neuron (6-10-51) with an average percentage error of 0.966% is said to be satisfactory and can be used for the purpose of line to line fault location. In the case of double line to ground fault, it is seen that the network configuration of 6-input neurons, 21- hidden neurons, 11- hidden neurons, 1-output (621-11-1), with an average percentage error of 1.122% to be satisfactory and can be used for the purpose of double line to ground fault location. While for the 3-phase fault, it is seen that the network configuration of 6-input neurons, 6-hidden neurons, 21-hidden neurons, 16-hidden neurons, 1-output neuron (6-6-21-16-1), with an average percentage error of 0.836% to be satisfactory and can be used for the purpose of 3-phase fault location on transmission lines.