Multi-domain feature fusion and machine learning-based SFRA diagnosis of transformer winding faults using a novel fault deviation index
TL;DR
A quantitative, measurement-oriented diagnostic framework based on the advanced normalized Multi-Domain Fault Deviation Index (MFDI) is proposed, which provides quantitative differentiation among the investigated fault conditions despite the high similarity of their corresponding spectral signatures.
Abstract
Sweep Frequency Response Analysis (SFRA) is one of the commonly used techniques for evaluating transformer winding integrity. However, the interpretation of the SFRA results remains qualitative and depends on expert evaluation. In this study, a quantitative, measurement-oriented diagnostic framework based on the advanced normalized Multi-Domain Fault Deviation Index (MFDI) is proposed. Within this method, magnitude, phase, impedance, and resonance-based descriptors are integrated in a unified formulation. The proposed method combines frequency band decomposition with multi-domain feature fusion to capture both local and global spectral deviations. This framework is experimentally validated under both intra-phase (U–N) and inter-phase (V–W) fault scenarios using SFRA measurements. The results demonstrate that the proposed MFDI captures the obvious difference between healthy and faulty states. For intra-phase fault scenarios, the proposed method provides quantitative differentiation among the investigated fault conditions despite the high similarity of their corresponding spectral signatures. In contrast, inter-phase faults generate more pronounced global spectral deviations, leading to highly separable feature representations for Support Vector Machine (SVM) and Random Forest (RF) classifiers. These findings demonstrate that the proposed approach advances SFRA from a predominantly qualitative interpretation technique toward a quantitative, interpretable, and machine-learning-compatible diagnostic methodology suitable for intelligent transformer condition monitoring.