International Journal of Computational
Intelligence Research (IJCIR)
Volume 3, Number 1 (2007)
Features selection of SVM and ANN using particle swarm optimization for power transformers incipient fault symptom diagnosis
Tsair-Fwu Lee, Ming-Yuan Cho
Department of Electrical engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, ROC.
Chang Gung Memorial Hospital-Kaohsiung Medical Center, Chang Gung University College of Medicine, Kaohsiung, Taiwan, ROC
For the purpose of incipient power transformer fault symptom diagnosis, a successful adaptation of the particle swarm optimization (PSO) algorithm to improve the performances of Artificial Neural Network (ANN) and Support Vector Machine (SVM) is presented in this paper. A PSO-based encoding technique is applied to improve the accuracy of classification, which removed redundant input features that may be confusing the classifier. Experiments using actual data demonstrated the effectiveness and high efficiency of the proposed approach, which makes operation faster and also increases the accuracy of the classification.
Feature, particle swarm optimization, incipient fault, diagnosis.