FRA-polar Net: A Novel Image Processing Framework for Transformer Condition Monitoring
Keywords:
Bode plot, Classification, Condition monitoring, Fault diagnosis, Feature extraction, Frequency response analysis (FRA), Image processing, Polar plot, Power systems, Transformer, winding deformationAbstract
This study proposes a novel approach to automate the interpretation of transformer frequency response analysis (FRA) data, addressing the inherent subjectivity of traditional methods. It introduces a framework that converts conventional FRA Bode plots into polar plots, which intuitively combine magnitude and phase information into a single, visually rich image. The study then evaluates the use of image processing techniques to analyze these polar plots for fault detection and classification. By simulating common transformer faults, such as winding deformation and short circuits, we demonstrate that each fault type produces a unique and identifiable pattern on the polar plot. The proposed methodology, which includes image preprocessing, feature extraction, and classification, achieves a remarkable 97.5% overall accuracy in diagnosing the transformer’s condition. A comparative analysis with traditional statistical methods, like the correlation coefficient, confirms the superior performance and objectivity of our image-based framework. The results indicate that this approach can significantly reduce reliance on expert judgment, improve diagnostic reliability, and enable the development of standardized automated systems for transformer condition monitoring.
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