Frequency response analysis: An enabling technology to detect internal faults within critical electric assets
Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the appl...
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my.utm.1009812023-05-23T10:22:16Z http://eprints.utm.my/id/eprint/100981/ Frequency response analysis: An enabling technology to detect internal faults within critical electric assets Al-Ameri, Salem Mgammal Alawady, Ahmed Allawy Abdul-Malek, Zulkurnain Ahmad Noorden, Zulkarnain Mohd. Yousof, Mohd. Fairouz Ahmed Salem, Ali Mosaad, Mohamed Ibrahim Abu-Siada, Ahmed TK Electrical engineering. Electronics Nuclear engineering Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the application of FRA technique to induction motors has not yet been fully explored. This paper presents analogical experimental studies for the application of FRA on power transformers and induction motors. For a consistent analogy, the FRA technique has been employed to detect short and open circuit turns in both appliances, which helps explore a wider scope of the FRA applications on rotating machines. In this regard, experimental FRA measurements are performed on an 11/0.415 kV, 500 kVA, three-phase distribution transformer and a 5.5 HP three-phase induction motor. Several short and open circuit faults are staged on the windings of both tested equipment and the FRA signature is recorded and compared with the reference signature at no fault. To quantify the impact of faults on the FRA signature, several statistical indicators are used and threshold limits for these indicators are proposed to automate the interpretation process. Results reveal a good correlation between the FRA signatures of induction motors and power transformers that attests to the feasibility of using FRA technique to detect various faults within large rotating machines. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100981/1/ZulkurnainAbdulMalek2022_FrequencyResponseAnalysisAnEnablingTechnology.pdf Al-Ameri, Salem Mgammal and Alawady, Ahmed Allawy and Abdul-Malek, Zulkurnain and Ahmad Noorden, Zulkarnain and Mohd. Yousof, Mohd. Fairouz and Ahmed Salem, Ali and Mosaad, Mohamed Ibrahim and Abu-Siada, Ahmed (2022) Frequency response analysis: An enabling technology to detect internal faults within critical electric assets. Applied Sciences (Switzerland), 12 (18). pp. 1-18. ISSN 2076-3417 http://dx.doi.org/10.3390/app12189201 DOI : 10.3390/app12189201 |
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TK Electrical engineering. Electronics Nuclear engineering Al-Ameri, Salem Mgammal Alawady, Ahmed Allawy Abdul-Malek, Zulkurnain Ahmad Noorden, Zulkarnain Mohd. Yousof, Mohd. Fairouz Ahmed Salem, Ali Mosaad, Mohamed Ibrahim Abu-Siada, Ahmed Frequency response analysis: An enabling technology to detect internal faults within critical electric assets |
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Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the application of FRA technique to induction motors has not yet been fully explored. This paper presents analogical experimental studies for the application of FRA on power transformers and induction motors. For a consistent analogy, the FRA technique has been employed to detect short and open circuit turns in both appliances, which helps explore a wider scope of the FRA applications on rotating machines. In this regard, experimental FRA measurements are performed on an 11/0.415 kV, 500 kVA, three-phase distribution transformer and a 5.5 HP three-phase induction motor. Several short and open circuit faults are staged on the windings of both tested equipment and the FRA signature is recorded and compared with the reference signature at no fault. To quantify the impact of faults on the FRA signature, several statistical indicators are used and threshold limits for these indicators are proposed to automate the interpretation process. Results reveal a good correlation between the FRA signatures of induction motors and power transformers that attests to the feasibility of using FRA technique to detect various faults within large rotating machines. |
format |
Article |
author |
Al-Ameri, Salem Mgammal Alawady, Ahmed Allawy Abdul-Malek, Zulkurnain Ahmad Noorden, Zulkarnain Mohd. Yousof, Mohd. Fairouz Ahmed Salem, Ali Mosaad, Mohamed Ibrahim Abu-Siada, Ahmed |
author_facet |
Al-Ameri, Salem Mgammal Alawady, Ahmed Allawy Abdul-Malek, Zulkurnain Ahmad Noorden, Zulkarnain Mohd. Yousof, Mohd. Fairouz Ahmed Salem, Ali Mosaad, Mohamed Ibrahim Abu-Siada, Ahmed |
author_sort |
Al-Ameri, Salem Mgammal |
title |
Frequency response analysis: An enabling technology to detect internal faults within critical electric assets |
title_short |
Frequency response analysis: An enabling technology to detect internal faults within critical electric assets |
title_full |
Frequency response analysis: An enabling technology to detect internal faults within critical electric assets |
title_fullStr |
Frequency response analysis: An enabling technology to detect internal faults within critical electric assets |
title_full_unstemmed |
Frequency response analysis: An enabling technology to detect internal faults within critical electric assets |
title_sort |
frequency response analysis: an enabling technology to detect internal faults within critical electric assets |
publisher |
MDPI |
publishDate |
2022 |
url |
http://eprints.utm.my/id/eprint/100981/1/ZulkurnainAbdulMalek2022_FrequencyResponseAnalysisAnEnablingTechnology.pdf http://eprints.utm.my/id/eprint/100981/ http://dx.doi.org/10.3390/app12189201 |
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13.201949 |