A neural network-based estimation of electric fields along high voltage insulators - Aydogmus, Zafer (Pergamon-Elsevier Science Ltd, 2009)
This paper presents a two-dimensional (2D) electric fields estimation program to calculate the field distribution along the leakage distance of an insulator under polluted conditions using artificial neural network (ANN). A fog type suspension insulator has been used for calculations. Two types of application are submitted for validation of the model. Firstly, electric fields have been calculated for different line voltages and pollution conductivities using finite element method (FEM). Some of the calculated data sets have been used for training the ANN and the other sets of data have been used for testing. The x, y coordinates and operating voltage have been used as inputs of ANN1 to estimate the electrical fields on the insulator surface for first application. And x. y coordinates and pollution level of the surface have been used as inputs of ANN2 to estimate the electrical fields on the insulator surface for second application. These developed models make it possible to determine the electrical fields easier and shortens the calculation time. The results show that the estimated values of electrical field have been obtained with high levels of accuracy in both models. Thus, presented ANN models can be used efficiently for designing and developing of the insulators for Various line voltages and pollution levels. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Neural networks; Electric fields; Pollution; Insulator