Power Transformer Fault Diagnosis Based on Rough Set Theory and Support Vector Machine
Wenqing Zhao, Yongli Zhu · 2007
Power transformers are one of the most expensive components of electrical power plants and the failures of such transformers can result in serious power system issues, so fault diagnosis for power transformer is very important to insure the whole power system run normally. Based on fault attributes of transformers, there are a few works have been done on transformer fault diagnosis using such methods as neural network,bayesian,and so on. As the fault information of power transformers has uncertainty characteristic, in this paper, a novel approach based on rough set theory and SVM is proposed. Moreover, by comparing with the traditional methods like the neural network, there is less fault data discriminated by the rough set theory and SVM model and the accuracy for power transformer fault diagnosis is improved using our proposed model.