Fault identification in HVDC systems with neural networks

This paper describes a neural network and its simulation results for fault diagnosis in HVDC systems. Fault diagnosis is carried out by mapping input data patterns, which represent the behaviour of the system, to one or more fault conditions. The behaviour of the converters is described in terms of the time varying patterns of conducting thyristors and AC and DC fault characteristics. A three-layer neural network consisting of 20 input nodes, 12 hidden nodes and 2 output nodes is used. This paper describes the performance of the network for AC and DC faults due to changes in number of hidden layers, number of neurons in the layer, learning rate and momentum. Dynamic characteristics of networks for different configurations are studied too. The time performance of the network is also included. Neural networks provide an effective way for fault diagnosis and identification. Simulation data obtained from the EMTP are used to test the performance of this network