A complex neural classifier for the fault prognosis and diagnosis of overhead electrical lines

Abstract

The technique proposed in this work is finalized to the non-intrusive monitoring of high voltage electrical networks. In order to develop a prognostic method capable of avoiding failures on overhead transmission grids, the connection joints between two sections of the line are considered. The method is based on the use of Frequency Response Analysis (FRA) and machine learning, represented by a neural classifier based on a Multi-Valued Neuron (MVN) neural network. The procedure can be considered as a smart measurement block, where a single measure is used by a neural classifier to extract information able to diagnose an electrical system. This means that the method shown in this paper can be developed and adapted to solve many different problems in the world of industry, such as the management of the most worn electrical devices. In this sense, the maintenance organization plays a fundamental role and the prognostic approach allows the reduction of the recovery times by locating critical components. This monitoring system increases the global availability of the electrical grid in which it is used and, from a practical point of view, it can be used by network operators to obtain online control of operating conditions.