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<p>Accurate assessment of winding faults in large power autotransformers using frequency response analysis (FRA) remains challenging due to the complex electromechanical coupling between winding geometry and the measured response. This paper proposes a graph-based diagnostic framework that links physics-based FRA modeling with probabilistic machine learning for fault type identification, localization, and severity estimation. First, a RLC state-space model of a 125 MVA, 230/132/20 kV power autotransformer is developed and validated against measured FRA traces in the healthy state. Subsequently, simulation-based faulty FRA datasets are then generated by applying mechanical deformations, including axial displacement (AD) and radial deformation (RD), and an electrical turn-to-turn short-circuit (TTSC) fault through corresponding geometry/parameter variations. Each FRA trace is converted into a graph representation using the weighted visibility-graph approach, and informative features are extracted from the resulting graph-domain spectrum. A Gaussian-process (GP) pipeline is subsequently employed, where Type-GPC performs multi-class fault type identification (Healthy/AD/RD/TTSC), Location-GPC localizes TTSC faults, and fault-specific GPR models estimate severity with quantified uncertainty. Experimental results show overall accuracies of 92.9 % for fault type identification and 92.0 % for TTSC localization. Severity estimation achieves MAE/RMSE of 1.00 %/1.34 % for AD/RD and 5.30&#xa0;<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\Omega\)</EquationSource></InlineEquation>/5.91&#xa0;<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\Omega\)</EquationSource></InlineEquation> for TTSC, demonstrating reliable and interpretable diagnostics.</p>

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Graph-based analysis of frequency response measurements for assessment of winding faults in power autotransformers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Vahid Tamjidi Azad,
  • Hadi Afsharirad,
  • Hamed Alizadeh Ghazijahani,
  • Amin Safari

摘要

Accurate assessment of winding faults in large power autotransformers using frequency response analysis (FRA) remains challenging due to the complex electromechanical coupling between winding geometry and the measured response. This paper proposes a graph-based diagnostic framework that links physics-based FRA modeling with probabilistic machine learning for fault type identification, localization, and severity estimation. First, a RLC state-space model of a 125 MVA, 230/132/20 kV power autotransformer is developed and validated against measured FRA traces in the healthy state. Subsequently, simulation-based faulty FRA datasets are then generated by applying mechanical deformations, including axial displacement (AD) and radial deformation (RD), and an electrical turn-to-turn short-circuit (TTSC) fault through corresponding geometry/parameter variations. Each FRA trace is converted into a graph representation using the weighted visibility-graph approach, and informative features are extracted from the resulting graph-domain spectrum. A Gaussian-process (GP) pipeline is subsequently employed, where Type-GPC performs multi-class fault type identification (Healthy/AD/RD/TTSC), Location-GPC localizes TTSC faults, and fault-specific GPR models estimate severity with quantified uncertainty. Experimental results show overall accuracies of 92.9 % for fault type identification and 92.0 % for TTSC localization. Severity estimation achieves MAE/RMSE of 1.00 %/1.34 % for AD/RD and 5.30 \(\Omega\)/5.91 \(\Omega\) for TTSC, demonstrating reliable and interpretable diagnostics.