Neural networks for power system condition monitoring and protection

To maintain a good voltage quality level to customers in power distribution system, it is essential to minimise transients, line voltage dips and spik es due to a variety of causes, including fault occurrence, power interrupti on and large load changes. In case of private generating systems it is esse ntial that ultra-rapid switching devices be used which cut off the customer plant from the utility system so quickly that the presence of voltage dips is not perceived by the industrial plant's sensitive loads. These devices require very fast acquisition and control systems which permit to diagnose and, possibly, predict abnormal events. In this paper, a control methodolog y based on a locally recurrent-globally feed-forward neural network and on a neural classifier is proposed. It will be shown that it is possible to pr edict with good accuracy the value of the control variables based on previo usly acquired samples and use these values to recognise the kind of abnorma l event that is about to occur on the network. (C) 1998 Elsevier Science B. V. All rights reserved.