Fault Diagnosis in HVDC Systems with Neural Networks

Abstract

This paper describes a neural network design and its simulation results for fault diagnosis in HVDC power 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, pulse zone periods, voltage zone periods and ac & dc fault characteristics. A three-layer neural network consisting of 24 input nodes, 12 hidden nodes and 13 output nodes are used. 13 different faults were considered and the neural network approach shows a great potential as an effective way for fault diagnosis.