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Kubayev, Ulugbek, Toshalieva, Saodat, Ayubov, Ilyos, Farxodjon, Murodov, Ergash ugli, Qodirov Farrux, Jakhongir Rasulovich, Zaynalov, Nietbaevich, Tlegenov Baxitbay, Bektemirov, Abduxamid Abdumalikovich, Seyranovna, Aliyeva Susanna, Haydarovich Kushatov, Rustam, Khurramova, Madina, Haydarovna, Rano Davlatova, Erkinjon, Tulovov. (1403). Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy. سامانه مدیریت نشریات علمی, 12(Special Issue), 33-42. doi: 10.22098/joape.2025.16153.2247
Ulugbek Kubayev; Saodat Toshalieva; Ilyos Ayubov; Murodov Farxodjon; Qodirov Farrux Ergash ugli; Zaynalov Jakhongir Rasulovich; Tlegenov Baxitbay Nietbaevich; Abduxamid Abdumalikovich Bektemirov; Aliyeva Susanna Seyranovna; Rustam Haydarovich Kushatov; Madina Khurramova; Rano Davlatova Haydarovna; Tulovov Erkinjon. "Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy". سامانه مدیریت نشریات علمی, 12, Special Issue, 1403, 33-42. doi: 10.22098/joape.2025.16153.2247
Kubayev, Ulugbek, Toshalieva, Saodat, Ayubov, Ilyos, Farxodjon, Murodov, Ergash ugli, Qodirov Farrux, Jakhongir Rasulovich, Zaynalov, Nietbaevich, Tlegenov Baxitbay, Bektemirov, Abduxamid Abdumalikovich, Seyranovna, Aliyeva Susanna, Haydarovich Kushatov, Rustam, Khurramova, Madina, Haydarovna, Rano Davlatova, Erkinjon, Tulovov. (1403). 'Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy', سامانه مدیریت نشریات علمی, 12(Special Issue), pp. 33-42. doi: 10.22098/joape.2025.16153.2247
Kubayev, Ulugbek, Toshalieva, Saodat, Ayubov, Ilyos, Farxodjon, Murodov, Ergash ugli, Qodirov Farrux, Jakhongir Rasulovich, Zaynalov, Nietbaevich, Tlegenov Baxitbay, Bektemirov, Abduxamid Abdumalikovich, Seyranovna, Aliyeva Susanna, Haydarovich Kushatov, Rustam, Khurramova, Madina, Haydarovna, Rano Davlatova, Erkinjon, Tulovov. Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy. سامانه مدیریت نشریات علمی, 1403; 12(Special Issue): 33-42. doi: 10.22098/joape.2025.16153.2247

Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy

Journal of Operation and Automation in Power Engineering
مقاله 5، دوره 12، Special Issue، 2024، صفحه 33-42    اصل مقاله (1.66 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2025.16153.2247
نویسندگان
Ulugbek Kubayev* 1؛ Saodat Toshalieva2؛ Ilyos Ayubov3؛ Murodov Farxodjon4؛ Qodirov Farrux Ergash ugli5؛ Zaynalov Jakhongir Rasulovich3؛ Tlegenov Baxitbay Nietbaevich6؛ Abduxamid Abdumalikovich Bektemirov4؛ Aliyeva Susanna Seyranovna3؛ Rustam Haydarovich Kushatov4؛ Madina Khurramova7؛ Rano Davlatova Haydarovna8؛ Tulovov Erkinjon9
1Kimyo International University in Tashkent, Uzbekistan.
2Termez State University, Termez, Uzbekistan
3Samarkand Institute of Economics and Service, Samarkand, Uzbekistan
4Samarkand State University Named after Sharof Rashidov, University Boulevard, Samarkand, Uzbekistan
5Shahrisabz State Pedagogical Institute, Shakhrisabz, Kashkadarya, Uzbekistan
6Department of Software Engineering and the Digital Economy, Nukus Innovation Institute, Uzbekistan
7International School of Finance and Technology, Tashkent Region, Kibrai District, University Street, Tashkent, Uzbekistan
8Navoi State Pedagogical Institute, Uzbekistan
9Tashkent State University of Economics, Tashkent, Uzbekistan
چکیده
The growing complexity of microgrid operations, driven by the integration of renewable energy sources and distributed generation, has heightened the need for more advanced islanding detection methods. Traditional techniques, such as passive and active methods, often struggle with accuracy in these dynamic environments. Passive methods can result in high false detection rates as they rely on system parameters like voltage and frequency, which are sensitive to fluctuations. Active methods, while generally more accurate, can introduce disturbances into the system and are often less effective in low-power scenarios. These limitations pose significant challenges to maintaining the stability and integrity of microgrids, underscoring the need for innovative approaches. To address these challenges, this paper presents a novel approach that combines deep learning with fuzzy logic for adaptive control in microgrids. Deep learning facilitates precise real-time data analysis, enabling the system to accurately detect islanding events as they occur. Meanwhile, fuzzy logic provides adaptable decision-making, allowing the system to respond effectively to changing conditions. This integration significantly enhances detection accuracy and reduces error rates compared to traditional techniques, ensuring reliable performance throughout the day. By offering a more robust and flexible solution, the proposed method not only improves fault detection but also enhances overall system stability, making it a valuable contribution to microgrid management. This approach addresses the critical need for more effective islanding detection in increasingly complex microgrid environments, paving the way for more resilient and reliable energy systems.
کلیدواژه‌ها
Microgrid operations؛ islanding detection؛ deep learning؛ fuzzy logic؛ adaptive control
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