Particle swarm optimization-based SVM application: Power transformers incipient fault syndrome diagnosis

Based on statistical learning theory, Support Vector Machine (SVM) has been well recognized as a powerful computational tool for problems with nonlinearity had high dimensionalities. In this paper, we present a successful adoption of the particle swarm optimization (PSO) algorithm to improve the performances of SVM classifier for the purpose of incipient, faults syndrome diagnosis of power transformers. A PSO-based encoding technique is applied to improve the accuracy of classification. The proposed scheme removes irreverent input features that may be confusing the classifier and optimizes the kernel parameters simultaneously. Experiments on real operational data demonstrated the effectiveness and high efficiency of the proposed approach which make operation faster and also increase the accuracy of the classification.