Neural network-based faulty line identification in power distribution systems

Artificial neural networks (ANNs) have large input-error tolerance ranges and can be used as classifiers. Utilizing this property, a neural network-based detector, which identifies the faulty line directly by taking current and voltage patterns as feature vectors, has been designed. The quality of classification is not dependent on the transmission model, but rather on the net topology, training set, and the choice of learning law. A feed-forward multilayer perceptron, using the Back-Propagation. Learning Algorithm, has been used to realize an optimal classifier. The classification quality, by simulating certain faults on the lines, has demonstrated the capability of the proposed approach for distribution pourer system protection.