HVDC systems fault diagnosis with neural networks

The authors describe 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 4 output nodes is used. 16 different faults have been considered and dynamic characteristics of networks for different configurations are also studied. The time performance of the network is also included. Neural networks provide an effective way for fault diagnosis