Failure Risk Prediction Using Artificial Neural Networks for Lightning Surge Protection of Underground MV Cables

Abstract

Lightning surge is actually being considered as one of the most dangerous events in power distribution systems. Basically, it hits the overhead distribution line then propagates to the other network components, such as underground cables and transformers. Due to lightning strokes, insulation failure of such components could occur. The failure risk can be determined on the basis of network configuration, its parameters, and surge arresters data. The determination of this index can greatly help in optimizing the network surge protection. The implementation of an artificial neural network (ANN) for prediction of the failure risk for underground medium-voltage cables connected to overhead distribution lines is introduced. The main advantage of ANN actually is the time and effort savings due to the random nature of the problem and extended calculation process. The calculation of the failure risk using ANN is applied to a group of industrial surge arresters. The results of the ANN test coincide with the analytical ones.