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This study applies fuzzy logic optimization to the IEEE Key Gas Method for diagnosing power transformer faults, aiming to improve predictive maintenance in electrical grids. While it involves algorithmic classification techniques relevant to machine learning applications, its primary subject is specialized industrial instrumentation and energy infrastructure rather than core AI or quantum technology development. Consequently, this does not meet TechPulse's scope requirements as the focus lies on general engineering diagnostics within the power sector. Others
Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis
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.
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.
This paper was presented at 2025 10th International Conference on Applying New Technology in Green Buildings (ATiGB). Please cite the published version
Artificial Intelligence (cs.AI)
arXiv:2608.18133 [cs.AI]
(or arXiv:2608.18133v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.18133 arXiv-issued DOI via DataCite (pending registration)
2025 10th International Conference on Applying New Technology in Green Buildings (ATiGB), Danang, Vietnam, 2025, pp. 114-119
https://doi.org/10.1109/ATiGB66719.2025.11142211 DOI(s) linking to related resources
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