SOLVING ELECTRIC POWER TRANSMISSION LINE FAULTS USING HYBRID ARTIFICIAL NEURAL NETWORK MODULES

Abstract

Purpose : This paper examined solving electric power transmission line faults using hybrid artificial neural network modules. This paper focuses on detecting, classifying and locating faults on electric power transmission lines. Methodology : The fundamental principle of the proposed fault diagnosis method is to add to the original network a fictitious bus where the fault occurs. Hence, the bus impedance matrix is augmented by one order. Then, the driving point impedance of the fault bus and the transfer impedances between this bus and other buses are expressed as functions of the unknown fault distance. Based on the definition of the bus impedance matrix, the change of the sequence voltage at any bus during the fault is formulated in terms of the corresponding transfer impedance and sequence fault current. Results: Findings from this study prove that back propagation neural networks are very efficient when a sufficiently large training data set is available. The regression plots of the various phases such as training, testing and validation indicate that the best linear fit very closely matches the ideal case with an overall correlation coefficient of 0.99329. The performance of the neural network in this case illustrates its ability to generalize and react upon new data. It is to be noted that the average error in this case is just 0.836 % which is still acceptable. Thus the neural networks performance is considered satisfactory and can be used for the purpose of three phase fault location as well.