3D approach for fault identification within power transformers using frequency response analysis

Ahmed Abu-Siada, Ibrahim Radwan, Ahmed F. Abdou

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.
Original languageEnglish
Pages (from-to)903-911
Number of pages9
JournalIET Science, Measurement and Technology
Volume13
Issue number6
DOIs
Publication statusPublished - 25 Jul 2019
Externally publishedYes

Cite this

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title = "3D approach for fault identification within power transformers using frequency response analysis",
abstract = "With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.",
keywords = "deformation, finite element analysis, transformer windings, power transformer testing, frequency response, fault diagnosis, feature extraction, fault identification, frequency response analysis quantification, aged power transformers, sweep frequency response analysis technique, mechanical integrity, current interpretation technique, current SFRA technique, incipient mechanical deformation, incipient faults, interpretation process, transformer winding deformations, finite element simulation analysis, digital image processing-based code, 3D-SFRA signature, three-dimensional SFRA signature, apparent power 40.0 MVA, apparent power 45.0 MVA",
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3D approach for fault identification within power transformers using frequency response analysis. / Abu-Siada, Ahmed; Radwan, Ibrahim; Abdou, Ahmed F.

In: IET Science, Measurement and Technology, Vol. 13, No. 6, 25.07.2019, p. 903-911.

Research output: Contribution to journalArticle

TY - JOUR

T1 - 3D approach for fault identification within power transformers using frequency response analysis

AU - Abu-Siada, Ahmed

AU - Radwan, Ibrahim

AU - Abdou, Ahmed F.

PY - 2019/7/25

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N2 - With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.

AB - With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.

KW - deformation

KW - finite element analysis

KW - transformer windings

KW - power transformer testing

KW - frequency response

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KW - feature extraction

KW - fault identification

KW - frequency response analysis quantification

KW - aged power transformers

KW - sweep frequency response analysis technique

KW - mechanical integrity

KW - current interpretation technique

KW - current SFRA technique

KW - incipient mechanical deformation

KW - incipient faults

KW - interpretation process

KW - transformer winding deformations

KW - finite element simulation analysis

KW - digital image processing-based code

KW - 3D-SFRA signature

KW - three-dimensional SFRA signature

KW - apparent power 40.0 MVA

KW - apparent power 45.0 MVA

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DO - 10.1049/iet-smt.2018.5573

M3 - Article

VL - 13

SP - 903

EP - 911

JO - IET Science, Measurement and Technology

JF - IET Science, Measurement and Technology

SN - 1751-8822

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ER -