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

• Published in: Polymers Vol. 10; no. 10; p. 1096
• Main Authors: Huang, Xiaoge, Zhang, Yiyi, Liu, Jiefeng, Zheng, Hanbo, Wang, Ke
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 03.10.2018; MDPI AG
• Subjects: artificial bee colony (ABC); dissolved gas analysis (DGA); fault diagnosis; genetic algorithm (GA); power transformers; simulated annealing (SA) algorithm; support vector machine (SVM); artificial bee colony (ABC); dissolved gas analysis (DGA); fault diagnosis; power transformers; support vector machine (SVM); genetic algorithm (GA); simulated annealing (SA) algorithm
• ISSN: 2073-4360; 2073-4360
• DOI: 10.3390/polym10101096

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.