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Azamovich, Azizov Tuxtamish, Erkinjon, Tulovov, Khalmirzaev, Mansur, Mukhitdinov, Otabek, Numanovich, Nizamov Akhtam, Sapaev, I.B., Rakhmonov, Toshmirza, Minovvarkhon Sabirovna, Yunusova, Mamatkulovich, Bobokulov Bakhromkul, Khakimboy ugli, Bobojonov Otabek, Tulakov, Ulugbek, Kholikov, Rаvshаn. (1403). Machine Learning-based Fault Detection and Classification in microgrid. سامانه مدیریت نشریات علمی, 12(Special Issue (Open)), 43-52. doi: 10.22098/joape.2025.16912.2315
Azizov Tuxtamish Azamovich; Tulovov Erkinjon; Mansur Khalmirzaev; Otabek Mukhitdinov; Nizamov Akhtam Numanovich; I.B. Sapaev; Toshmirza Rakhmonov; Yunusova Minovvarkhon Sabirovna; Bobokulov Bakhromkul Mamatkulovich; Bobojonov Otabek Khakimboy ugli; Ulugbek Tulakov; Rаvshаn Kholikov. "Machine Learning-based Fault Detection and Classification in microgrid". سامانه مدیریت نشریات علمی, 12, Special Issue (Open), 1403, 43-52. doi: 10.22098/joape.2025.16912.2315
Azamovich, Azizov Tuxtamish, Erkinjon, Tulovov, Khalmirzaev, Mansur, Mukhitdinov, Otabek, Numanovich, Nizamov Akhtam, Sapaev, I.B., Rakhmonov, Toshmirza, Minovvarkhon Sabirovna, Yunusova, Mamatkulovich, Bobokulov Bakhromkul, Khakimboy ugli, Bobojonov Otabek, Tulakov, Ulugbek, Kholikov, Rаvshаn. (1403). 'Machine Learning-based Fault Detection and Classification in microgrid', سامانه مدیریت نشریات علمی, 12(Special Issue (Open)), pp. 43-52. doi: 10.22098/joape.2025.16912.2315
Azamovich, Azizov Tuxtamish, Erkinjon, Tulovov, Khalmirzaev, Mansur, Mukhitdinov, Otabek, Numanovich, Nizamov Akhtam, Sapaev, I.B., Rakhmonov, Toshmirza, Minovvarkhon Sabirovna, Yunusova, Mamatkulovich, Bobokulov Bakhromkul, Khakimboy ugli, Bobojonov Otabek, Tulakov, Ulugbek, Kholikov, Rаvshаn. Machine Learning-based Fault Detection and Classification in microgrid. سامانه مدیریت نشریات علمی, 1403; 12(Special Issue (Open)): 43-52. doi: 10.22098/joape.2025.16912.2315

Machine Learning-based Fault Detection and Classification in microgrid

Journal of Operation and Automation in Power Engineering
دوره 12، Special Issue (Open)، 2024، صفحه 43-52    اصل مقاله (1.59 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2025.16912.2315
نویسندگان
Azizov Tuxtamish Azamovich* 1؛ Tulovov Erkinjon2؛ Mansur Khalmirzaev3؛ Otabek Mukhitdinov4؛ Nizamov Akhtam Numanovich5؛ I.B. Sapaev6، 7؛ Toshmirza Rakhmonov8؛ Yunusova Minovvarkhon Sabirovna9؛ Bobokulov Bakhromkul Mamatkulovich5؛ Bobojonov Otabek Khakimboy ugli10؛ Ulugbek Tulakov11؛ Rаvshаn Kholikov12
1Vice-Rector for Scientific Affairs and Innovation, International School of Finance Technology and Science, Uzbekistan.
2Tashkent State University of Economics, Tashkent, Uzbekistan
3Department of "Digital Economy", Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan.
4Kimyo International University in Tashkent , Shota Rustaveli Street 156, 100121, Тashkent, Uzbekistan.
5Department of Network Economics, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan.
6Department Physics and Chemistry, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Tashkent, Uzbekistan.
7Scientific University of Tashkent for Applied Sciences, Street Gavhar 1, Tashkent 100149, Uzbekistan.
8Department of Digital Economy, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan.
9Department of General Sciences and Culture, Tashkent State University of Law, Uzbekistan.
10Urganch State University, Uzbekistan.
11Termez State University, Termez, Uzbekistan.
12Department of Fundamental Economic Science of the International School of Finance Technology and Science, Uzbekistan.
چکیده
Fault Detection and Classification plays a vital role in maintaining the reliability and stability of microgrids, especially as they incorporate renewable energy sources and become more decentralized. Microgrids face a wide variety of faults, such as short circuits, line-to-ground faults, and other disturbances, which can negatively affect system performance. Traditional fault detection methods have primarily focused on False Data Injection and cyber-attacks, emphasizing vulnerabilities in communication infrastructure. However, this study addresses current faults within the electrical network, focusing on system stability and real-time fault detection in the absence of communication-related errors. In this work, machine learning techniques are employed to enhance fault classification accuracy. Partial Least Squares is used for feature selection to extract relevant statistical features from real-time current data collected from various microgrid components. By optimizing these features and applying them to machine learning models, the approach overcomes the limitations of conventional fault detection methods. The results show a significant improvement in fault classification performance, with up to 10% higher accuracy compared to traditional methods. Additionally, the use of data from neighboring microgrid components boosts the model's robustness, adaptability, and performance under varying operational conditions, contributing to a more resilient microgrid. This research introduces an innovative approach to fault detection in microgrids by combining machine learning and feature optimization, offering a more accurate, reliable, and efficient solution to ensure continuous energy supply and improve system stability under different fault scenarios.
کلیدواژه‌ها
Fault detection؛ feature selection؛ fault classification؛ data-driven modeling؛ system stability؛ short circuit faults
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