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Journals / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2026 / Volume: 68 Issue: 1

Comparison of Artificial Intelligence Techniques and Conventional Techniques for Power Transformer Fault Diagnosis with Dissolved Gas Analysis (DGA): Challenges and Opportunities

Nurullah Açıkgöz(ANKARA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0003-4328-5758
Murat Sazlı(ANKARA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0001-9235-3679
Pages
—
Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In the study, future challenges and opportunities are elaborately presented for researchers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In power systems, transformers have a key role in terms of electrical energy sustainability in transmission and distribution.
📊
Key Findings & Data:Moreover, this crucial instrument of power systems must be operated at optimum performance in electric transmission lines and power distribution system as well as rational economic conditions.
💡
Conclusion & Impact:In the study, future challenges and opportunities are elaborately presented for researchers.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Power transformer · artificial intelligence techniques · DGA

Abstract

In power systems, transformers have a key role in terms of electrical energy sustainability in transmission and distribution. Moreover, this crucial instrument of power systems must be operated at optimum performance in electric transmission lines and power distribution system as well as rational economic conditions. When usual operating conditions is became, mineral oil comes out on a slow and typical degradation. If on the contrary due to thermal or electrical stress, the degradation status accelarates. In this case, some gases (ethylene,acetylene,carbonmonoxide,carbondioxied, hydrogen, methane,ethane) are occured in the mineral oil. For dissolved gas analysis (DGA), custom traditional methods seems to identify and classify the initial faults. However, they are unable to notice the status in case of multiple electrical and thermal errors simultaneously. While the serious limitation of conventional DGA methods in terms of accuracy and consistency, AI methods gives well prediction. In the study, future challenges and opportunities are elaborately presented for researchers.

🔖

Cite This Article

Açıkgöz, N., Sazlı, M. (2026). Comparison of Artificial Intelligence Techniques and Conventional Techniques for Power Transformer Fault Diagnosis with Dissolved Gas Analysis (DGA): Challenges and Opportunities. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Volume: 68 Issue: 1. https://doi.org/10.33769/aupse.1757488

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