Standardization of DGA interpretation techniques using fuzzy logic approach

Hmood, S. and Abu-Siada, A. and Masoum, Mohammad A.S. and Islam, Syed M. 2012. Standardization of DGA interpretation techniques using fuzzy logic approach, in Proceedings of the 2012 International Conference on Condition Monitoring and Diagnosis, Sep 23-27 2012, pp. 929-932. Bali, Indonesia: IEEE.

Source Title

IEEE

Source Conference

International Conference on Condition Monitoring and Diagnosis

DOI

10.1109/CMD.2012.6416305

Remarks

Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

URI

http://hdl.handle.net/20.500.11937/7332

Collection

Curtin Research Publications

Abstract

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation. As a result, various DGA interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. DGA interpretation is yet a challenge in the power transformer condition monitoring research area. To alleviate this issue, this paper introduces a fuzzy logic approach to help in standardizing DGA results quantification and classification using various interpretation techniques such as key gas, Rogers ratio, IEC ratio, Doernenburg and Duval triangle methods. In this context, DGA results for 2000 oil samples have been collected from different transformers of different ratings, life span and operating conditions. Traditional DGA interpretation techniques are used to analyze the results which are then compared with the results of the fuzzy logic models. Results show that the fuzzy logic models enhance the consistency among all current interpretation techniques and can eliminate the need for expert personal to interpret DGA results.