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Abstract
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tIn this paper, a comprehensive review on methods for voltage sag source location is presented and alsonine generalized methods using positive sequence phasors, instantaneous positive sequence compo-nents, Clarke’s components and integration are introduced. Most discussed methods use single criteria,and as results will show, their accuracy is limited. Therefore, this paper proposes another novel methodusing a robust support vector machine (SVM) in which many features are extracted, based on previouslydescribed methods. Then, the source location by machine learning technique is discussed with two stepsin detail and the SVM with the linear, polynomial, and radial basis function (RBF) kernels are applied alongwith optimal genetic search. Also, the k-fold cross validation is used to prevent over fitting. The effect ofprincipal component analysis (PCA) is investigated, too. A comparative analysis is performed between theexisting methods, the nine generalized methods and the novel method, by applying extensive numericalsimulations in a Brazilian regional utility, by using PSCAD/EMTDC and MATLAB. Finally, effectiveness ofall methods was obtained, reactive power based on generalized methods using instantaneous positivesequence components and Clarke’s components gave the right location in 88% of total simulated cases,whereas the robust SVM based method with RBF kernel without PCA had the highest accuracy (95%).
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