Performance evaluation of online transformer internal fault detection based on transient overvoltage signals
© 1994-2012 IEEE. Winding deformation is a critical power transformer issue that needs to be detected as soon as it emerges due to its progressive nature and severe consequences it may lead to. Frequency response analysis (FRA) is the only reliable technique currently used to detect such deformatio...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/67724 |
| _version_ | 1848761641499361280 |
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| author | Zhao, X. Yao, C. Zhao, Z. Abu-Siada, Ahmed |
| author_facet | Zhao, X. Yao, C. Zhao, Z. Abu-Siada, Ahmed |
| author_sort | Zhao, X. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 1994-2012 IEEE. Winding deformation is a critical power transformer issue that needs to be detected as soon as it emerges due to its progressive nature and severe consequences it may lead to. Frequency response analysis (FRA) is the only reliable technique currently used to detect such deformation. However, the technique is conducted offline that may cause interruption to the electricity grid when taking an operating transformer out of service for testing. Moreover, as the current FRA technique is only conducted on suspected units, a mechanical fault may emerge and progress before the utility becomes aware of. To overcome these limitations, this paper presents a practical feasibility study to detect transformer winding deformations online. The technique relies on utilizing transient overvoltage signals that a transformer is subjected to during its normal operation as a natural variable frequency excitation source. The feasibility of utilizing various transient overvoltage signals as excitation source for transformer FRA signature measurement is firstly investigated and optimum signal parameters are recommended based on signal voltage and energy spectrum analyses. Secondly, the proposed technique is verified through simulation analysis by comparing the transformer frequency response signatures when transient overvoltage and conventional sweep frequency voltage are used as excitation sources. Also, experimental testing is conducted to assess the feasibility of the proposed technique to identify various winding deformations such as radial buckling, telescoping and short circuit turns. Custom-made capacitive voltage divider is developed to accurately detect the response of transient overvoltage signals generated due to environmental conditions or power system switching operations. The proposed technique along with the developed capacitive voltage divider is implemented on a 3-phase Wye-Delta, 50 Hz, 31.5 MVA, 110/10.5 kV power transformer to measure the frequency response signatures of the high voltage winding. |
| first_indexed | 2025-11-14T10:34:54Z |
| format | Journal Article |
| id | curtin-20.500.11937-67724 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:34:54Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-677242018-05-18T08:06:38Z Performance evaluation of online transformer internal fault detection based on transient overvoltage signals Zhao, X. Yao, C. Zhao, Z. Abu-Siada, Ahmed © 1994-2012 IEEE. Winding deformation is a critical power transformer issue that needs to be detected as soon as it emerges due to its progressive nature and severe consequences it may lead to. Frequency response analysis (FRA) is the only reliable technique currently used to detect such deformation. However, the technique is conducted offline that may cause interruption to the electricity grid when taking an operating transformer out of service for testing. Moreover, as the current FRA technique is only conducted on suspected units, a mechanical fault may emerge and progress before the utility becomes aware of. To overcome these limitations, this paper presents a practical feasibility study to detect transformer winding deformations online. The technique relies on utilizing transient overvoltage signals that a transformer is subjected to during its normal operation as a natural variable frequency excitation source. The feasibility of utilizing various transient overvoltage signals as excitation source for transformer FRA signature measurement is firstly investigated and optimum signal parameters are recommended based on signal voltage and energy spectrum analyses. Secondly, the proposed technique is verified through simulation analysis by comparing the transformer frequency response signatures when transient overvoltage and conventional sweep frequency voltage are used as excitation sources. Also, experimental testing is conducted to assess the feasibility of the proposed technique to identify various winding deformations such as radial buckling, telescoping and short circuit turns. Custom-made capacitive voltage divider is developed to accurately detect the response of transient overvoltage signals generated due to environmental conditions or power system switching operations. The proposed technique along with the developed capacitive voltage divider is implemented on a 3-phase Wye-Delta, 50 Hz, 31.5 MVA, 110/10.5 kV power transformer to measure the frequency response signatures of the high voltage winding. 2017 Journal Article http://hdl.handle.net/20.500.11937/67724 10.1109/TDEI.2017.006772 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | Zhao, X. Yao, C. Zhao, Z. Abu-Siada, Ahmed Performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| title | Performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| title_full | Performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| title_fullStr | Performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| title_full_unstemmed | Performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| title_short | Performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| title_sort | performance evaluation of online transformer internal fault detection based on transient overvoltage signals |
| url | http://hdl.handle.net/20.500.11937/67724 |