期刊
IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION
卷 24, 期 6, 页码 3443-3451出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TDEI.2017.006841
关键词
Duval Pentagon 1; transformer oil; dissolved gas analysis; combined algorithm; support vector machine; particle swarm optimization; K-nearest neighbor; fault diagnosis
The carried out investigations deal with the application of machine learning algorithms to Duval Pentagon 1 graphical method for the diagnosis of transformer oil. In fact, combined to graphical methods, pattern recognition aims to may complement. For this purpose, we have used the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) algorithms combined to the Duval method. The SVM parameters have been optimized with the Particle Swarm Optimization (PSO). Inspired from IEC and IEEE, five classes namely PD, D1, D2, T1&T2, and T3 have been adopted. The combined algorithms were verified using 155 samples from IEC TC 10 and related databases. We found that KNN, SVM may complement the Duval Pentagon 1 diagnosis method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据