Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation
IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a compa...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronic Engineers
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/59587 |
| _version_ | 1848760520527577088 |
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| author | Zhao, Z. Yao, C. Li, C. Islam, Syed |
| author_facet | Zhao, Z. Yao, C. Li, C. Islam, Syed |
| author_sort | Zhao, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types. |
| first_indexed | 2025-11-14T10:17:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-59587 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:17:05Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronic Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-595872018-04-05T02:53:51Z Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation Zhao, Z. Yao, C. Li, C. Islam, Syed IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types. 2017 Journal Article http://hdl.handle.net/20.500.11937/59587 10.1109/TIE.2017.2752135 Institute of Electrical and Electronic Engineers restricted |
| spellingShingle | Zhao, Z. Yao, C. Li, C. Islam, Syed Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation |
| title | Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation |
| title_full | Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation |
| title_fullStr | Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation |
| title_full_unstemmed | Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation |
| title_short | Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation |
| title_sort | detection of power transformer winding deformation using improved fra based on binary morphology and extreme point variation |
| url | http://hdl.handle.net/20.500.11937/59587 |