A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA-SA-SVM OFC Selection and ABC-SVM Classifier

被引：13

作者：

Huang, Xiaoge ^{[1
,2
]}

Zhang, Yiyi ^{[1
,2
]}

Liu, Jiefeng ^{[1
,2
]}

Zheng, Hanbo ^{[1
,2
]}

Wang, Ke ^{[3
]}

机构：

[1] Guangxi Univ, Guangxi Key Lab Power Syst Optimizat & Energy Tec, Nanning 530004, Peoples R China

[2] Guangxi Univ, Natl Demonstrat Ctr Expt Elect Engn Educ, Nanning 530004, Peoples R China

[3] China Elect Power Res Inst, Beijing 100192, Peoples R China

来源：

POLYMERS | 2018年 / 10卷 / 10期

基金：

中国国家自然科学基金;

关键词：

artificial bee colony (ABC); dissolved gas analysis (DGA); fault diagnosis; genetic algorithm (GA); power transformers; simulated annealing (SA) algorithm; support vector machine (SVM); IEC TC 10; OIL ANALYSIS; ALGORITHM; MODEL; PERFORMANCE; DATABASES; NETWORKS; DGA;

D O I：

10.3390/polym10101096

中图分类号：

O63 [高分子化学（高聚物）];

学科分类号：

070305 ; 080501 ; 081704 ;

摘要：

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers' online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA-SA-SVM and the ABC-SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.

引用

页数：20