اخبار و اعلانات

 آمار

تعداد نشریات31
تعداد شماره‌ها848
تعداد مقالات8,174
تعداد مشاهده مقاله16,076,355
تعداد دریافت فایل اصل مقاله10,604,156
Sezavar, Hamid, Karimi, Hamid, Fahimi, Navid. (1405). A Comparative Analysis of AI Algorithms for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis. نشریه علمی دانشگاه سیستان و بلوچستان, (), -. doi: 10.22111/ieco.2026.54596.1744
Hamid Reza Sezavar; Hamid Karimi; Navid Fahimi. "A Comparative Analysis of AI Algorithms for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis". نشریه علمی دانشگاه سیستان و بلوچستان, , , 1405, -. doi: 10.22111/ieco.2026.54596.1744
Sezavar, Hamid, Karimi, Hamid, Fahimi, Navid. (1405). 'A Comparative Analysis of AI Algorithms for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis', نشریه علمی دانشگاه سیستان و بلوچستان, (), pp. -. doi: 10.22111/ieco.2026.54596.1744
Sezavar, Hamid, Karimi, Hamid, Fahimi, Navid. A Comparative Analysis of AI Algorithms for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis. نشریه علمی دانشگاه سیستان و بلوچستان, 1405; (): -. doi: 10.22111/ieco.2026.54596.1744

A Comparative Analysis of AI Algorithms for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

International Journal of Industrial Electronics Control and Optimization
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 09 تیر 1405    اصل مقاله (766.39 K)
نوع مقاله: Research Articles
شناسه دیجیتال (DOI): 10.22111/ieco.2026.54596.1744
نویسندگان
Hamid Reza Sezavar* 1؛ Hamid Karimi1؛ Navid Fahimi2
1Qom University of Technology, Qom, Iran
2Iran University of Science and Technology, Tehran, Iran
چکیده
A comparative approach is pretend in this paper that evaluates different Artificial Intelligence (AI) methods for diagnosing power transformer faults using Dissolved Gas Analysis (DGA). Traditional approaches like the Rogers Ratio Method and Duval Triangle have been used for many years, but offer unreliable results for complex cases. Although, newer AI methods present better results, but still vary in how well they work. In this paper, several AI approaches are evaluated including Support Vector Machines (SVMs), Random Forest (RF), Gradient Boosting Machines (GBMs), Deep Neural Networks (DNNs) and a new combinational model is proposed based on comparing results. A real DGA dataset is used for covering six different fault types for the proposed testing. The results show that while all AI methods do better than traditional approaches, the combinational approach performs the best with 92.3% accuracy. This is found 20.2% better than traditional methods and 4.8% better than the best single AI model. Rational explanation is provided for how each method works and practical recommendation is presented for choosing the right approach based on particular requirements and available resources in real practices.
کلیدواژه‌ها
Transformer Diagnosis؛ Dissolved Gas Analysis؛ Machine Learning Comparison؛ Hybrid AI Models؛ Fault Classification
آمار
تعداد مشاهده مقاله: 23
تعداد دریافت فایل اصل مقاله: 39


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب