DGA Method-Based ANFIS Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil

Mohd Bin Yaacob, Ahmed Hussein and Mohd Bin Othman

Modern Applied Science, 2015, vol. 10, issue 1, 13

Abstract: Accurate fault diagnostics and assessment of electrical power transformer insulation oil for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through early and precise diagnosis of faults and through efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role in this regard. The dissolved gas analysis (DGA) in oil is a reliable method for diagnosing faults and assessing insulation oil quality in transformers. The safeguarding teams of transformer power stations often suffer from the occurrence of sudden faults, which result in severe damages and heavy monetary loss. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and to assess the status and quality of insulation oil in power transformers. A suitable treatment was identified using the Rogers ratio method depending on the DGA in oil. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm is capable of assessing oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.

Date: 2015
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/48188/29896 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/48188 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:10:y:2015:i:1:p:13

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().