Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis
Kim-Anh NguyenHuy Hoang LeBa Tu Phung
Aug 2026
Artificial Intelligence
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.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.
Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al.· Heliyon· 4 citations· ⚡1
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al.· SIAM Journal on Scientific C...· 3 citations
This work presents AutoOR, a scalable synthetic data generation and reinforcement learning pipeline that trains LLMs to autoformulate optimization problems specified in natural language across linear, mixed-integer, and non-linear categories and introduces a curriculum RL strategy that bootstraps from limited initial training data to make this class tractable for post-training.
S. Motwani, Chuan Du, A. Petrov et al.· 2 citations
A benchmark built on the Speech Accessibility Project (SAP) dataset is introduced that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech, finding that current models do not meaningfully use this context.
P. Moure, Niclas Pokel, Bilal Bounajma et al.· arXiv.org· 2 citations
