A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements

Bonginkosi A. Thango, Agha F. Nnachi, Goodness A. Dlamini and Pitshou N. Bokoro
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Bonginkosi A. Thango: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa
Agha F. Nnachi: Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa
Goodness A. Dlamini: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa
Pitshou N. Bokoro: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa

Energies, 2022, vol. 15, issue 7, 1-22

Abstract: A frequency response analysis (FRA) is a well-known technique for evaluating the mechanical stability of a power transformer’s active part components. FRA’s measuring practices have been industrialised and are codified in IEEE and IEC standards. However, because there is no valid coding in the standard, the interpretation of FRA data is still far from being a widely acknowledged and authoritative approach. This study proposes an innovative fault segmentation and localisation technique based on FRA data. The algorithm is based on regression analysis to estimate the repeatability and relationship between the FRA fingerprint and the latest measured data. Initially, the measuring frequency is discretised into three regions to narrow the location of the fault; the regression model of the fingerprint and current FRA data are then evaluated. As a benchmark, two statistical indicators are the employed benchmark against the proposed method. Finally, the proposed scheme identifies and characterises various transformer conditions, such as healthy windings, axial and radial winding deformations, core deformation and electrical faults. The database used in this study consists of FRA measurements from 70 mineral-oil-immersed power transformers of different designs, ratings and manufacturers that were physically inspected for various faults and comparable frequency regions. The results achieved corroborate the efficacy of the proposed regression analysis fault recognition algorithm (RAFRA) model for transformer fault diagnosis using FRA. Further recommendations are made to address the reproducibility concerns induced by multiple FRA testing conditions.

Keywords: frequency response analysis (FRA); power transformer; regression analysis; numerical indicators (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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