TY  - EJOU
AU  - Yue, Xinyu 
AU  - Li, Zhenhua 
AU  - Li, Zhenxing 
AU  - Zhang, Tao 
AU  - Xu, Yanchun 
AU  - Zhao, Xiaozhen 

TI  - Online Monitoring Method for Transformer Winding Deformation Based on Three-Dimensional Lissajous Curves
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - Winding deformation is a predominant cause of transformer failures and critically compromises the safe, reliable, and economic operation of power systems. To overcome the inadequacy of the conventional three-dimensional (3D) Lissajous curve method in discriminating among various types of winding faults, this paper proposes an online monitoring method for transformer winding deformation based on 3D Lissajous curves. In the proposed method, the primary current <mml:math id="mml-ieqn-1"><mml:mrow><mml:mtext>d</mml:mtext></mml:mrow><mml:msub><mml:mi>i</mml:mi><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mrow><mml:mtext>d</mml:mtext></mml:mrow><mml:mi>t</mml:mi></mml:math>, the derivative of the primary current <mml:math id="mml-ieqn-2"><mml:mrow><mml:mtext>d</mml:mtext></mml:mrow><mml:msub><mml:mi>i</mml:mi><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mrow><mml:mtext>d</mml:mtext></mml:mrow><mml:mi>t</mml:mi></mml:math>, and the voltage difference between the primary and secondary sides <mml:math id="mml-ieqn-3"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi></mml:mrow><mml:mi>u</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> are adopted as the coordinate axes to construct 3D Lissajous curves. Through simulation modeling, the variation patterns of the curve shape characteristics are qualitatively analyzed under both sinusoidal and non-sinusoidal (including harmonic) excitation conditions. Furthermore, by integrating covariance matrix analysis with adaptive particle swarm optimization (APSO), characteristic parameters corresponding to different fault types are extracted and subsequently input into a support vector machine (SVM) for fault classification and identification. Simulation results demonstrate that the proposed method achieves a fault classification accuracy of 98.15% and exhibits high classification stability under diverse operating conditions. This study presents a novel technical approach for online monitoring and fault-type discrimination of transformer winding deformation.
KW  - Power transformer; winding deformation; online detection; characteristic graph method

DO  - 10.32604/ee.2026.077395