Hybrid RVM–ANFIS algorithm for transformer fault diagnosis. Issue 14 (2nd August 2017)

• Record Type: Journal Article
• Authors: Fan, Jingmin; Wang, Feng; Sun, Qiuqin; Bin, Feng; Liang, Fangwei; Xiao, Xuanyi
• Is Part Of: IET generation, transmission & distribution. Volume 11:Issue 14(2017)
• Page Start: 3637
• Page End: 3643
• Publication Date: 2017-08-02
• Subjects: support vector machines -- fault diagnosis -- power transformers -- fuzzy reasoning -- fuzzy neural nets -- power engineering computing; power transformer fault diagnosis -- hybrid RVM‐ANFIS algorithm -- dissolved gas analysis -- DGA -- ambiguous characteristic analysis -- relevance vector machine -- adaptive neural fuzzy inference system -- binary separation -- support vector machine -- artificial neural network; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.0547
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Dissolved gas analysis (DGA) is a popular method for diagnosing faults inside power transformers. However, some of the recorded data for the analysis are with ambiguous characteristic, leading to misdiagnosis of conventional methods. In this work, a hybrid method, which combines the relevance vector machine (RVM) and the adaptive neural fuzzy inference system (ANFIS) has been proposed to address this issue. Given the fuzziness between DGA records and fault type, and to minimise the number of rules that ANFIS needs to extract, the RVM algorithm performs binary separation firstly, and then ANFIS is utilised to achieve further fault diagnosis in this study. The experimental results demonstrate that the hybrid RVM–ANFIS algorithm can achieve an accuracy rate as high as 95%. Moreover, the proposed algorithm exceeds single ANFIS, support vector machine, and artificial neural network on distinguishing multiple faults and samples with ambiguous characteristic. The engineering application results also demonstrate the effectiveness and superiority of the proposed RVM–ANFIS.