Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis

A new study from arXiv (2608.18133v1) presents FL-KGM, a fuzzy logic-enhanced version of the IEEE Key Gas Method for diagnosing power transformer faults via dissolved gas analysis, achieving up to 98.6% accuracy on real-world datasets. The method introduces refined membership functions, optimized fuzzy rule sets, and separate CO/CO2 handling to overcome limitations of the standard KGM and other fuzzy logic approaches.

arXiv:2608.18133v1 Announce Type: new Abstract: Reliable transformer fault diagnosis is essential for maintaining power system stability. The IEEE Key Gas Method KGM , a widely utilized approach in Dissolved Gas Analysis DGA , exhibits limitations in addressing ambiguous data and ensuring high diagnostic accuracy. This study presents An enhanced model combining Fuzzy Logic with the IEEE Key Gas Method FL-KGM that introduces refined membership functions, optimized fuzzy rule sets, and a novel separation of CO and CO2 to eliminate diagnostic inconsistencies. By leveraging multidimensional gas ratio analysis and an adaptive classification framework, FL-KGM delivers superior fault identification and classification. Experimental validation utilizing real-world datasets demonstrates that FL-KGM achieves up to 98.6% accuracy, significantly outperforming KGM and other FL-based approaches. These findings elucidate the potential of FL-KGM in advancing transformer monitoring, enabling intelligent fault detection, and enhancing predictive maintenance strategies in modern power systems.