An adaptive multi-fuzzy logic model for diagnosing transformer faults using dynamic weight optimization

Abstract:Dissolved gas analysis (DGA) is crucial for diagnosing early power transformer failures. Traditional DGA interpretation methods like Duval Triangle, IEC ratio, Roger ratio, Doernenburg ratio and Key Gas are inconsistent and vary in accuracy, especially for multiple fault conditions. We propose an Adaptive Multi-Fuzzy Logic (AMFL) model integrating multiple DGA methods with fuzzy logic and a dynamic weight adjustment mechanism. Unlike existing approaches with fixed weights, this system iteratively evaluates each method's diagnostic performance, identifies multiple fault types, and adjusts weights based on fault prediction accuracy. A feedback-based optimization recalibrates weights after each cycle to ensure optimal solution convergence. The model, implemented in MATLAB/Simulink, is validated against DGA datasets with known error conditions. Results show the AMFL model significantly improves diagnostic accuracy, especially in complex error scenarios, and enhances adaptability to new datasets. Comparative analysis demonstrates the proposed method outperforms traditional fixed weight multi-fuzzy systems in accuracy, consistency, and reliability of error detection. This work provides a robust, flexible diagnostic tool for transformer condition monitoring and supports more accurate asset management decisions.

This paper has been published in e-Prime - Advances in Electrical Engineering, Electronics and Energy. Please cite the published version

Machine Learning (cs.LG)

arXiv:2607.23486 [cs.LG]

(or arXiv:2607.23486v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.23486 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

e-Prime _ Advances in Electrical Engineering, Electronics and Energy. Volume 13, September 2025, 101048

https://doi.org/10.1016/j.prime.2025.101048 Focus to learn more DOI(s) linking to related resources

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