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 spikes due to a variety of causes, including fault occurrence, power interruption and large load changes. In case of private generating systems it is essential 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 methodology based on a locally recurrent-globally feedforward neural network and on a neural classifier is proposed. It will be shown that it is possible to predict with good accuracy the value of the control variables based on previously acquired samples and use these values to recognise the kind of abnormal event that is about to occur on the network.