A linear approach to determining an SVM-based fault locator’s optimal parameters

Abstract

The setting up process for support vector machines (SVM) is discussed in this paper. Such settings are normally obtained from exhaustive testing of SVM, settled on by using several configuration parameter values and evaluating performance by using techniques such as cross validation. The linear approach presented in this paper is based on redefining the classical SVM se-cond-order objective function. Better setting parameters were obtained by using low computational cost methodology for resol-ving new linear optimisation. The proposed approach was applied to a typical classification problem regarding fault location in power distribution systems; the results so obtained were compared to those obtained using classical methodology. An 80% im-provement was achieved in mean error when estimating fault location and 56% reduction in the computing time needed for ob-taining the best results when using classical approaches.

Keywords

• euclidean distance norm
• linear programming
• fault location
• support vector machine