Application of artificial neural networks for optimization of electrode contour

In this paper artificial neural networks (NN) with supervised learning are proposed for HV electrode optimization. To demonstrate the effectiveness of artificial NN in electric field problems, a simple cylindrical electrode system is designed first where the stresses can be computed analytically. It is found that once trained, the NN can give results with mean absolute error of ~1% when compared with analytically obtained results. In the next section of the paper, a multilayer feedforward NN with a back-propagation algorithm is designed for electrode contour optimization. The NN is first trained with the results of electric field computations for some predetermined contours of an axisymmetric electrode arrangement. Then the trained NN is used to give an optimized electrode contour in such a way that a desired stress distribution is obtained on the electrode surface. The results from the present study show that the trained NN can give optimized electrode contours to get a desired stress distribution on the electrode surface very efficiently and accurately