A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network

The work described in this paper addresses the problems encountered by conventional techniques in fault type classification in double-circuit transmission lines; these arise principally due to the mutual coupling between the two circuits under fault conditions, and this mutual coupling is highly variable in nature. It is shown that a neural network based on combined unsupervised/supervised training methodology provides the ability to accurately classify the fault type by identifying different patterns of the associated voltages and currents. The technique is compared with that based solely on a supervised training algorithm (i.e. backpropagation network classifier). It is then tested under different fault types, location, resistance and inception angle; different source capacities and load angles are also considered. All the test results show that the proposed fault classifier is very well suited for classifying fault types in double-circuit lines.