Enhanced Fuzzy Logic Boosts Transformer Fault Diagnosis Accuracy to 98.6%.

Maintaining the stability of power systems relies heavily on accurate fault diagnosis in transformers. The existing IEEE Key Gas Method (KGM), while widely used in Dissolved Gas Analysis (DGA), struggles with ambiguous data and often lacks high diagnostic precision. This research introduces an improved model, Fuzzy Logic with the IEEE Key Gas Method (FL-KGM), designed to overcome these limitations. FL-KGM incorporates refined fuzzy logic membership functions, optimized rule sets, and a novel method for separating carbon monoxide and carbon dioxide to eliminate diagnostic inconsistencies. By employing multidimensional gas ratio analysis and an adaptive classification framework, the model significantly enhances fault identification and classification capabilities. Experimental validation using real-world datasets demonstrated that FL-KGM achieved an impressive accuracy of up to 98.6%, significantly outperforming both the traditional KGM and other fuzzy logic-based approaches.