An alternative approach to fault location on power distribution feeders with embedded remote-end power generation using artificial neural networks

Abstract

In this paper, the design and implementation of a feed-forward artificial neural network (ANN)-based fault locator to classify and locate shunt faults on primary overhead power distribution lines with load taps and embedded remote-end power generation is presented. In the ANN algorithm, the standard back-propagation technique with a sigmoid activation function is used. The fault locator utilizes fault voltage and current samples obtained at a single location of a typical radial distribution system. The ANNs are trained with data under a wide variety of fault conditions and used for the fault type classification and fault location on the distribution line. A 34.5 kV distribution system is simulated using electro-magnetic transients program and their results are used to train and test the ANNs. The ANN-based fault locator gives high accuracy for the vast majority of the practically encountered systems and fault conditions, including the presence of load taps and the remote-end in-feed source.