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Artificial Intelligence for Assessing Composite Insulator Pollution Level: A Study on Partial Discharge Characteristics | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 25 خرداد 1404 اصل مقاله (1.01 M) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2025.51554.1680 | ||
| نویسندگان | ||
| Hamid Reza Sezavar؛ Saeed Hasanzadeh* | ||
| Department of electrical and computer Engineering, Qom university of Technology, Qom, Iran | ||
| چکیده | ||
| Insulator pollution levels are critical for ensuring the operational stability and safety of power transmission systems. Traditional methods for detecting pollution are often invasive, inaccurate, and time-consuming. To address these issues, this study investigates the application of Artificial Intelligence (AI), specifically Gradient Boosting Machines (GBM), to classify insulator pollution levels based on Partial Discharge (PD) characteristics. We utilize a combination of time-domain and frequency-domain features extracted from PD signals to train a predictive model. The results indicate that the proposed model achieves a high classification accuracy, averaging between 92% and 95% across various contamination levels. Furthermore, the study analyzes the model's sensitivity to environmental factors, including humidity and Hydrophobicity Class (HC), revealing important insights that could influence classification performance. By employing this AI-driven approach, we aim to significantly enhance the efficiency of power grid maintenance, ultimately contributing to the long-term stability and reliability of transmission systems. The findings from this research underscore the potential of AI in revolutionizing pollution assessment methods and optimizing maintenance practices in power infrastructure. | ||
| کلیدواژه‌ها | ||
| Artificial Intelligence؛ Composite Insulators؛ Gradient Boosting Machines؛ Partial Discharge؛ Pollution Assessment | ||
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آمار تعداد مشاهده مقاله: 32 تعداد دریافت فایل اصل مقاله: 30 |
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