Open AccessHybrid RVM–ANFIS algorithm for transformer fault diagnosis
Author(s) -
Fan Jingmin,
Wang Feng,
Sun Qiuqin,
Bin Feng,
Liang Fangwei,
Xiao Xuanyi
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0547
Subject(s) - adaptive neuro fuzzy inference system , relevance vector machine , artificial neural network , computer science , support vector machine , dissolved gas analysis , artificial intelligence , inference system , algorithm , transformer , data mining , machine learning , pattern recognition (psychology) , fuzzy logic , engineering , fuzzy control system , voltage , electrical engineering , transformer oil
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.