Estimation of time-to-flashover characteristics of contaminated electrolytic surfaces using a neural network

A major field of neural networks (NN) application is function estimation, because the useful properties of NN such as adaptivity and nonlinearity are well suited to function estimation tasks where the equation describing the function is unknown. In this paper the prerequisite training data are obtained from experimental studies performed on a flat plate model for a polluted insulator under power frequency voltage. Detailed studies have been carried to determine the NN parameters which give the best results. Studies have also been carried out to assess the effect of the presence of inadequate data in the training set on modeling accuracy. It is found that, when training is completed, NN is capable of estimating the function t = f(V, L, R(p)) very efficiently and effectively even when the inadequate data are incorporated in the training set.