A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches

Meysam Beheshti Asl, Issouf Fofana (), Fethi Meghnefi, Youssouf Brahami and Joao Pedro Da Costa Souza
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Meysam Beheshti Asl: Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada
Issouf Fofana: Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada
Fethi Meghnefi: Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada
Youssouf Brahami: Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada
Joao Pedro Da Costa Souza: Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada

Energies, 2025, vol. 18, issue 5, 1-38

Abstract: Frequency Response Analysis (FRA) is a proven method for detecting mechanical faults in transformers, such as winding deformations and short circuits. However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bias and lead to inconsistent results. Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. This integration can automate diagnostics, enhance accuracy, improve predictive maintenance, reduce reliance on expert interpretation and curtail operational costs. This paper reviews the application of FRA and ML alongside complementary techniques for transformer winding health assessment.

Keywords: frequency response analysis; FRA; transformer diagnostics; machine learning; winding faults; deformation analysis; deep learning; numerical indices; power transformer monitoring (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: 2025
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