Diagnosis of incipient fault of power transformers using SVM with clonal selection algorithms optimization

Authors:
Tsair-Fwu Lee;Ming-Yuan Cho;Chin-Shiuh Shieh;Hong-Jen Lee;Fu-Min Fang
Affiliations:
National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan , ROC;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan , ROC;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan , ROC;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
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

Citing 3
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Choosing Multiple Parameters for Support Vector Machines

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study we explore the feasibility of applying Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to the prediction of incipient power transformer faults. A clonal selection algorithm (CSA) is introduced for the first time in the literature to select optimal input features and RBF kernel parameters. CSA is shown to be capable of improving the speed and accuracy of classification systems by removing redundant and potentially confusing input features, and of optimizing the kernel parameters simultaneously. Simulation results on practice data demonstrate the effectiveness and high efficiency of the proposed approach.