A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis
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 current techniques rely on personnel experience more than analytical formulation. As a result, the current t...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Elsevier Ltd
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/13790 |
| _version_ | 1848748440989728768 |
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| author | Abu-Siada, Ahmed Hmood, S |
| author_facet | Abu-Siada, Ahmed Hmood, S |
| author_sort | Abu-Siada, Ahmed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers. |
| first_indexed | 2025-11-14T07:05:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-13790 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:05:05Z |
| publishDate | 2015 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-137902017-09-13T15:02:13Z A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis Abu-Siada, Ahmed Hmood, S Transformer diagnosis Fuzzy logic Transformer failure DGA 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 current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers. 2015 Journal Article http://hdl.handle.net/20.500.11937/13790 10.1016/j.ijepes.2014.12.017 Elsevier Ltd restricted |
| spellingShingle | Transformer diagnosis Fuzzy logic Transformer failure DGA Abu-Siada, Ahmed Hmood, S A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| title | A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| title_full | A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| title_fullStr | A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| title_full_unstemmed | A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| title_short | A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| title_sort | new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis |
| topic | Transformer diagnosis Fuzzy logic Transformer failure DGA |
| url | http://hdl.handle.net/20.500.11937/13790 |