Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks-Reference-Cited by-同舟云学术

Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks

Published:2024-01-07 Issue:2 Volume:17 Page:296
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Mateus Balduíno César¹²^ORCID,Farinha José Torres³⁴^ORCID,Mendes Mateus³⁵^ORCID

Affiliation:

1. Research Centre in Asset Management and Systems Engineering, RCM²⁺ Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal

2. CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal

3. Instituto Superior de Engenharia de Coimbra, Polytechnic Institute of Coimbra, RCM²⁺ Research Centre in Asset Management and System Engineering, 3030-199 Coimbra, Portugal

4. Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal

5. Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal

Abstract

Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians.

Funder

RCM2+

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Link

https://www.mdpi.com/1996-1073/17/2/296/pdf

Reference65 articles.

1. An artificial neural network approach to transformer fault diagnosis;Zhang;IEEE Trans. Power Deliv.,1996

2. A Markovian model for power transformer maintenance;Liang;Int. J. Electr. Power Energy Syst.,2018

3. Identification of Catastrophic Failures in Power System Using Pattern Recognition and Fuzzy Estimation;Hazra;IEEE Trans. Power Syst.,2009

4. Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic;Huang;IEEE Trans. Dielectr. Electr. Insul.,2013

5. Causes of transformer failures and diagnostic methods—A review;Christina;Renew. Sustain. Energy Rev.,2018