A multiclass SVM-based classifier for transformer fault diagnosis using a particle swarm optimizer with time-varying acceleration coefficients

SUMMARY A multiclass support vector machine (SVM) classifier based upon particle swarm optimization (PSO) with time-varying acceleration coefficients for fault diagnosis of power transformers is proposed in this paper. The one-against-one combination scheme is adopted to extend SVM for settling the multiclass classification problem. The algorithm of PSO with time-varying acceleration coefficients (PSO-TVAC) is employed to optimize the parameters for SVM. The results show that the convergence of the PSO-TVAC algorithm is relatively faster and much more precise than that of the classical PSO. Moreover, compared with other reference diagnosis approaches, the results demonstrate the improved classification accuracy based upon the proposed approach and show it can be used as an effective tool for fault diagnosis of power transformers. Copyright © 2011 John Wiley & Sons, Ltd.

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