English

Abstract

Diagnosis techniques based on the dissolved gas analysis (DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using Area Under Receiver Operating Characteristics Curve (AUROCC)-based Genetic Fuzzy SVM fusion model. Recently, the use of Receiver Operating Characteristic (ROC) Curve and the area under the ROC Curve called Area Under Receiver Operating Characteristics Curve (AUROCC) has been receiving much attention as a measure of the performance of machine learning algorithms. In this paper, we propose a SVM classifier fusion model using genetic fuzzy system. Genetic algorithms are applied to tune the optimal fuzzy membership functions. The performance of SVM classifiers are evaluated by their AUROCCs. Our experiments show that AUROCC-based genetic fuzzy SVM fusion model produces not only better AUROCC but also better accuracy than individual SVM classifiers