Hybrid RVM–ANFIS algorithm for transformer fault diagnosis. Issue 14 (2nd August 2017)
- Record Type:
- Journal Article
- Title:
- Hybrid RVM–ANFIS algorithm for transformer fault diagnosis. Issue 14 (2nd August 2017)
- Main Title:
- Hybrid RVM–ANFIS algorithm for transformer fault diagnosis
- Authors:
- Fan, Jingmin
Wang, Feng
Sun, Qiuqin
Bin, Feng
Liang, Fangwei
Xiao, Xuanyi - 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.
- Is Part Of:
- IET generation, transmission & distribution. Volume 11:Issue 14(2017)
- Journal:
- IET generation, transmission & distribution
- Issue:
- Volume 11:Issue 14(2017)
- Issue Display:
- Volume 11, Issue 14 (2017)
- Year:
- 2017
- Volume:
- 11
- Issue:
- 14
- Issue Sort Value:
- 2017-0011-0014-0000
- 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 - Journal URLs:
- http://digital-library.theiet.org/content/journals/iet-gtd ↗
http://ieeexplore.ieee.org/servlet/opac?punumber=4082359 ↗
http://www.ietdl.org/IET-GTD ↗
https://ietresearch.onlinelibrary.wiley.com/journal/17518695 ↗
http://www.theiet.org/ ↗ - DOI:
- 10.1049/iet-gtd.2017.0547 ↗
- Languages:
- English
- ISSNs:
- 1751-8687
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4363.252540
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16596.xml