Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis

TL;DR

This paper introduces a feature-weighted domain adaptation method combining MMD and CORAL, tailored for fault diagnosis in power transformers, significantly improving transfer accuracy across different operational conditions.

Contribution

It proposes a novel feature-weighted domain adaptation technique that enhances model transferability in power transformer fault diagnosis by prioritizing features with larger distributional shifts.

Findings

Achieves 7.9% better accuracy than Fine-Tuning.

Outperforms MMD-CORAL by 2.2%.

Demonstrates robustness across various training sample sizes.

Abstract

Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely on heuristic rules, which may lead to inconsistent results. Machine learning (ML)-based approaches have improved diagnostic accuracy; however, power transformers operate under varying conditions, and differences in transformer type, environmental factors, and operational settings create distribution shifts in diagnostic data. Consequently, direct model transfer between transformers often fails, making techniques for domain adaptation a necessity. To tackle this issue, this work proposes a feature-weighted domain adaptation technique that combines Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) with feature-specific weighting (MCW). Kolmogorov-Smirnov (K-S) statistics are used to assign adaptable weights, prioritizing features with larger distributional discrepancies and thereby improving source and target domain alignment. Experimental evaluations on datasets for power transformers demonstrate the effectiveness of the proposed method, which achieves a 7.9% improvement over Fine-Tuning and a 2.2% improvement over MMD-CORAL (MC). Furthermore, it outperforms both techniques across various training sample sizes, confirming its robustness for domain adaptation.