Improved Power Transformer Incipient Fault Detection Using a New Gas Ratio and Decision Tree Algorithm based on Dissolved Gas Analysis | ||
| International Journal of Research and Technology in Electrical Industry | ||
| مقاله 8، دوره 3، شماره 1، 2024، صفحه 309-316 اصل مقاله (1.27 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.48308/ijrtei.2024.236235.1051 | ||
| نویسندگان | ||
| Milad Shafiei asl* ؛ Farhad Haghjoo | ||
| Faculty of Electrical Engineering, Shahid Abbaspour School of Eng., Shahid Beheshti University, Tehran, Iran | ||
| چکیده | ||
| Power transformers are critical and costly components of power systems, necessitating effective fault detection methods to ensure their reliability and longevity. This paper introduces a new gas ratio (NGR) method utilizing a decision tree (DT) algorithm to detect incipient faults in transformers through dissolved gas analysis (DGA). By incorporating ten gas ratios, the proposed method enhances fault detection accuracy while maintaining simplicity and ease of use. The DT was developed and evaluated using a dataset from the Egyptian Electricity Holding Company, demonstrating superior performance compared to traditional methods. The results indicate that the NGR method significantly improves fault detection accuracy, especially in identifying partial discharge (PD) and high-temperature overheating (T3) faults. This approach offers a promising solution for practical transformer maintenance and fault diagnosis. This research highlights the potential of integrating advanced machine learning techniques with traditional DGA methods to improve fault detection accuracy, reduce maintenance costs, and enhance the reliability of power transformers. | ||
| کلیدواژهها | ||
| Power transformers؛ Fault detection؛ Dissolved gas analysis؛ Gas ratios؛ Machine learning؛ Decision tree | ||
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آمار تعداد مشاهده مقاله: 105 تعداد دریافت فایل اصل مقاله: 173 |
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