Analysis of high-voltage substations design using artificial neural networks

Abstract

This paper demonstrates that artificial neural networks can be used effectively for the identification and estimation of parameters related to analysis and design of high-voltage substations. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage in substations taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. Examples of simulation of tests are presented to validate the proposed approach. The results that were obtained by experimental evidences and numerical simulations allowed the proposition of new rules about the specification of substations.