Fixed least squares support vector machines for flashover modelling of outdoor insulators

Abstract

Many theoretical and experimental studies have been performed to optimize the maintenance times of outdoor insulators. But most of the proposed methods are both time consuming and difficult to apply in practice and also they rely mainly on pollution charts and they are not reliable to weather variations. Artificial intelligence has been extensively applied to solve a large number of electrical and high voltage engineering modeling and optimization problems. In this paper, a methodology based on modified LS-SVM strategy using a fixed set of support vectors is proposed to evaluate the flashover performance of outdoor insulators under contaminated conditions, where the candidate support vectors are selected from the training set according to a quadratic Renyi criterion. The obtained results are promising and ensure that the presented technique can help high voltage engineers to assess insulator performance including such important factors as the flashover behavior, the insulator geometrical parameters, aging, and contamination accumulation. Further comparative analysis of the estimated results with the measured data collected from the site measurement demonstrate the effectiveness of the use of fixed-size LS-SVMs models for flashover prediction.