A Novel Online Technique to Detect Power Transformer Winding Faults
Frequency-response analysis (FRA) has been growing in popularity in recent times as a tool to detect mechanical deformation within power transformers. To conduct the test, the transformer has to be taken out of service which may cause interruption to the electricity grid. Moreover, because FRA relie...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
IEEE Power Engineering Society
2012
|
| Online Access: | http://hdl.handle.net/20.500.11937/15534 |
| _version_ | 1848748918681108480 |
|---|---|
| author | Abu-Siada, Ahmed Islam, Syed |
| author_facet | Abu-Siada, Ahmed Islam, Syed |
| author_sort | Abu-Siada, Ahmed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Frequency-response analysis (FRA) has been growing in popularity in recent times as a tool to detect mechanical deformation within power transformers. To conduct the test, the transformer has to be taken out of service which may cause interruption to the electricity grid. Moreover, because FRA relies on graphical analysis, it calls for an expert to analyze the results. As so far, there is no standard code for FRA interpretation worldwide. In this paper, a novel online technique is introduced to detect the internal faults within a power transformer by constructing the voltage-current (V - I) locus diagram to provide a current state of the transformer. The technique does not call for any special equipment as it uses the existing metering devices attached to any power transformer to monitor the input voltage, output voltage, and the input current at the power frequency and, hence, online monitoring can be realized. Various types of faults have been simulated to assess its impact on the proposed locus. A Matlab code based on digital image processing is developed to calculate any deviation of the V - I locus with respect to the reference one and to identify the type of fault. The proposed technique is easy to be implemented and automated so that the requirement for expert personnel can be eliminated. |
| first_indexed | 2025-11-14T07:12:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-15534 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:12:41Z |
| publishDate | 2012 |
| publisher | IEEE Power Engineering Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-155342017-09-13T13:41:03Z A Novel Online Technique to Detect Power Transformer Winding Faults Abu-Siada, Ahmed Islam, Syed Frequency-response analysis (FRA) has been growing in popularity in recent times as a tool to detect mechanical deformation within power transformers. To conduct the test, the transformer has to be taken out of service which may cause interruption to the electricity grid. Moreover, because FRA relies on graphical analysis, it calls for an expert to analyze the results. As so far, there is no standard code for FRA interpretation worldwide. In this paper, a novel online technique is introduced to detect the internal faults within a power transformer by constructing the voltage-current (V - I) locus diagram to provide a current state of the transformer. The technique does not call for any special equipment as it uses the existing metering devices attached to any power transformer to monitor the input voltage, output voltage, and the input current at the power frequency and, hence, online monitoring can be realized. Various types of faults have been simulated to assess its impact on the proposed locus. A Matlab code based on digital image processing is developed to calculate any deviation of the V - I locus with respect to the reference one and to identify the type of fault. The proposed technique is easy to be implemented and automated so that the requirement for expert personnel can be eliminated. 2012 Journal Article http://hdl.handle.net/20.500.11937/15534 10.1109/TPWRD.2011.2180932 IEEE Power Engineering Society restricted |
| spellingShingle | Abu-Siada, Ahmed Islam, Syed A Novel Online Technique to Detect Power Transformer Winding Faults |
| title | A Novel Online Technique to Detect Power Transformer Winding Faults |
| title_full | A Novel Online Technique to Detect Power Transformer Winding Faults |
| title_fullStr | A Novel Online Technique to Detect Power Transformer Winding Faults |
| title_full_unstemmed | A Novel Online Technique to Detect Power Transformer Winding Faults |
| title_short | A Novel Online Technique to Detect Power Transformer Winding Faults |
| title_sort | novel online technique to detect power transformer winding faults |
| url | http://hdl.handle.net/20.500.11937/15534 |