Publication result detail

Adaptive Neuro-Fuzzy Inference System for Power Oil Transformer Diagnostics

JANDA, O.; HAMMER, M.; MINISTR, M.; ERTL, J.

Original Title

Adaptive Neuro-Fuzzy Inference System for Power Oil Transformer Diagnostics

English Title

Adaptive Neuro-Fuzzy Inference System for Power Oil Transformer Diagnostics

Type

Peer-reviewed article not indexed in WoS or Scopus

Original Abstract

The adaptive neuro-fuzzy inference system for the diagnostics of power oil trans-formers is described in this paper. The system is based on the DGA methods where the ANFIS system is applied on the results. A new fuzzy system can be created or the original model of the diagnostic method according to the IEC 60599 standard can be adapted. The real data of gas concentrations was used for the learning process. The resulting accuracy of the optimized fuzzy model achieved up to 78%

English abstract

The adaptive neuro-fuzzy inference system for the diagnostics of power oil trans-formers is described in this paper. The system is based on the DGA methods where the ANFIS system is applied on the results. A new fuzzy system can be created or the original model of the diagnostic method according to the IEC 60599 standard can be adapted. The real data of gas concentrations was used for the learning process. The resulting accuracy of the optimized fuzzy model achieved up to 78%

Keywords

DGA, ANFIS, Fuzzy, IEC ratio, Transformer

Key words in English

DGA, ANFIS, Fuzzy, IEC ratio, Transformer

Authors

JANDA, O.; HAMMER, M.; MINISTR, M.; ERTL, J.

RIV year

2012

Released

05.10.2011

Publisher

MM publishing

Location

Praha

ISBN

1803-1269

Periodical

MM Science Journal

Volume

4

Number

3

State

Czech Republic

Pages from

325

Pages to

331

Pages count

7

BibTex

@article{BUT74336,
  author="Ondřej {Janda} and Miloš {Hammer} and Martin {Ministr} and Jakub {Ertl}",
  title="Adaptive Neuro-Fuzzy Inference System for Power Oil Transformer Diagnostics",
  journal="MM Science Journal",
  year="2011",
  volume="4",
  number="3",
  pages="325--331",
  issn="1803-1269"
}