3D approach for fault identification within power transformers using frequency response analysis. Issue 6 (1st August 2019)

• Record Type: Journal Article
• Authors: Abu‐Siada, Ahmed; Radwan, Ibrahim; Abdou, Ahmed F.
• Is Part Of: IET science, measurement & technology. Volume 13:Issue 6(2019)
• Page Start: 903
• Page End: 911
• Publication Date: 2019-08-01
• Subjects: 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; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2018.5573
• Languages: English
• ISSNs: 1751-8822

Abstract

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.