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This is a Literature Review study.

October 12, 2025Elektronika ir ElektrotechnikaOpen Access

A Comprehensive Review of Transformer Fault Diagnosis Studies Based on Dissolved Gas Analysis: Classical Methods, Historical Development of the Devices Used, Artificial Intelligence Based Methods, Accuracy of Classifications of Predictions

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Artificial intelligence techniques improve prediction accuracy in transformer fault diagnosis, surpassing traditional methods.
Traditional dissolved gas analysis struggles with identifying simultaneous electrical or thermal faults, highlighting a critical limitation.
Parameter optimisation is crucial for eliminating dataset imbalance, leading to enhanced classification accuracy in AI-driven diagnoses.
This review serves as a comprehensive literature source, combining classical methods and modern AI approaches for future research initiatives.

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Implication

This review highlights how dissolved gas analysis improves transformer fault diagnosis accuracy using classical and AI methods, emphasizing dataset optimization.

Cite This Study

Acikgoz et al. (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff2402 https://doi.org/10.5755/j02.eie.39824

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Implication

This review highlights how dissolved gas analysis improves transformer fault diagnosis accuracy using classical and AI methods, emphasizing dataset optimization.

A Comprehensive Review of Transformer Fault Diagnosis Studies Based on Dissolved Gas Analysis: Classical Methods, Historical Development of the Devices Used, Artificial Intelligence Based Methods, Accuracy of Classifications of Predictions

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Cite This Study

A Comprehensive Review of Transformer Fault Diagnosis Studies Based on Dissolved Gas Analysis: Classical Methods, Historical Development of the Devices Used, Artificial Intelligence Based Methods, Accuracy of Classifications of Predictions

Key Points

Discussion

Implication

Cite This Study

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