آمار

تعداد نشریات31
تعداد شماره‌ها453
تعداد مقالات4,019
تعداد مشاهده مقاله6,763,773
تعداد دریافت فایل اصل مقاله4,524,634
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
مراجع
  1. L. Xu, S. Wang, Z. Wang, and X. Qi, “Dual-layer self-healing strategy for standalone building energy systems: A case study of a tropical island,” Energy Build., vol. 283, p. 112827, 2023.
  2. Z. Peng, G. Zhou, C. Pan, H. Ren, A. L. Swindlehurst, P. Popovski, and G. Wu, “Channel estimation for ris-aided multi-user mmwave systems with uniform planar arrays,” IEEE Trans. Commun., vol. 70, no. 12, pp. 8105–8122, 2022.
  3. S. Mohsen, M. H. Nezhad, A. Fereidunian, H. Lesani, and M. H. Gavgani, “Enhancement of self-healing property of smart grid in islanding mode using electric vehicles and direct load control,” in 2014 Smart Grid Conf., pp. 1–6, IEEE, 2014.
  4. A. Younesi, H. Shayeghi, P. Siano, and A. Safari, “A multiobjective resilience-economic stochastic scheduling method for microgrid,” Int. J. Electr. Power Energy Syst., vol. 131, p. 106974, 2021.
  5. K. Khalili, I. Pourkeivani, and M. Abedi, “Micro-grids resilience enhancement using self-healing capability,” in 2019 Int. Power Syst. Conf., pp. 750–756, IEEE, 2019.
  6. S. Moradi, G. Zizzo, S. Favuzza, and F. Massaro, “A stochastic approach for self-healing capability evaluation in active islanded ac/dc hybrid microgrids,” Sustainable Energy Grids Networks, vol. 33, p. 100982, 2023.
  7. Y. Koraz and A. Gabbar, “Risk analysis and self-healing approach for resilient interconnect micro energy grids,” Sustainable Cities Soc., vol. 32, pp. 638–653, 2017.
  8. X. Chen and Y. Li, “A nondestructive islanding detection method based on adaptive and periodic disturbance on reactive power output of inverter-based distributed generation,” J. Appl. Math., vol. 2014, no. 1, p. 132348, 2014.
  9. H. Chen and J. Ye, “An improved active phase-shift islanding detection method based on fuzzy adaptive pid algorithm,” Int. J. Power Energy Syst., vol. 43, no. 10, 2023.
  10. E. J. Estébanez, V. M. Moreno, A. Pigazo, M. Liserre, and A. Dell’Aquila, “Performance evaluation of active islandingdetection algorithms in distributed-generation photovoltaic systems: Two inverters case,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1185–1193, 2010.
  11. N. Khosravi, M. Dowlatabadi, and K. Sabzevari, “A hierarchical deep learning approach to optimizing voltage and frequency control in networked microgrid systems,” Appl. Energy, vol. 377, p. 124313, 2025.
  12. R. Kumari and B. K. Naick, “Enhancing protection in ac microgrids: An adaptive approach with ann and anfis models,” Comput. Electr. Eng., vol. 115, p. 109103, 2024.
  13. O. Akbulut, M. Cavus, M. Cengiz, A. Allahham, D. Giaouris, and M. Forshaw, “Hybrid intelligent control system for adaptive microgrid optimization: Integration of rule-based control and deep learning techniques,” Energies, vol. 17, no. 10, p. 2260, 2024.
  14. S.-R. Yan, W. Guo, A. Mohammadzadeh, and S. Rathinasamy, “Optimal deep learning control for modernized microgrids,” Appl. Intell., vol. 53, no. 12, pp. 15638–15655, 2023.
  15. M. Zaben, M. Y. Worku, M. A. Hassan, and M. A. Abido, “Machine learning methods for fault diagnosis in ac microgrids: A systematic review,” IEEE Access, 2024.
  16. A. Jaiswal, S. Chandra, A. Priyadarshi, S. Sachan, and S. Deb, “Reinforcement learning based islanding detection technique in distributed generation,” Energy Rep., vol. 9, pp. 6006–6019, 2023.
  17. R. Aazami, M. Shoaei, A. Moradkhani, M. Shirkhani, A. ELrashidi, and K. M. AboRas, “Deep neural networks based method to islanding detection for multi-sources microgrid,” Energy Rep., vol. 11, pp. 2971–2982, 2024.
  18. W. Ali, A. Ulasyar, M. U. Mehmood, A. Khattak, K. Imran, H. S. Zad, and S. Nisar, “Hierarchical control of microgrid using iot and machine learning based islanding detection,” IEEE Access, vol. 9, pp. 103019–103031, 2021.
  19. W. Sun, S. Ma, I. Alvarez-Fernandez, R. Roofegari nejad, and A. Golshani, “Optimal self-healing strategy for microgrid islanding,” IET Smart Grid, vol. 1, no. 4, pp. 143–150, 2018.
  20. S. Talari, M. R. Haghifam, and A. Akhavein, “Optimization of the microgrid scheduling with considering contingencies in an uncertainty environment,” 2013.
  21. Z. Wang and J. Wang, “Self-healing resilient distribution systems based on sectionalization into microgrids,” IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3139–3149, 2015.
  22. M. N. Ambia, K. Meng, W. Xiao, and Z. Y. Dong, “Nested formation approach for networked microgrid self-healing in islanded mode,” IEEE Trans. Power Delivery, vol. 36, no. 1, pp. 452–464, 2020.
  23. N. Afsari, S. SeyedShenava, and H. Shayeghi, “A milp model incorporated with the risk management tool for self-healing oriented service restoration,” J. Oper. Autom. Power Eng., vol. 12, no. 1, pp. 1–13, 2024.
  24. N. Kumar, S. Dahiya, and K. Singh Parmar, “Multi-objective economic emission dispatch optimization strategy considering battery energy storage system in islanded microgrid,” J. Oper. Autom. Power Eng., vol. 12, no. 4, pp. 296–311, 2024.
  25. I. Sepehrirad, R. Ebrahimi, E. Alibeiki, and S. Ranjbar, “Thevenin impedance concept for fast detection of microgrid islanding scenarios in the presence of small synchronous generators,” J. Oper. Autom. Power Eng., vol. 12, no. 2, pp. 91–106, 2024.
  26. O. A. Allan and W. G. Morsi, “A new passive islanding detection approach using wavelets and deep learning for grid-connected photovoltaic systems,” Electr. Power Syst. Res., vol. 199, p. 107437, 2021.
  27. Binod ghimire (2024). Islanding Detection In Distributed Generation (https://www.mathworks.com/matlabcentral/fileexchange/62672-islanding-detection-in-distributed-generation), MATLAB Central File Exchange. Retrieved September 1, 2024.
  28. K. Khalili, I. Pourkeivani, and M. Abedi, “Micro-grids resilience enhancement using self-healing capability,” in 2019 Int. Power Syst. Conf.., pp. 750–756, IEEE, 2019.
  29. N. Malwar, S. Kadwane, and R. Somalwar, “Review on study of active islanding detection technique in distributed generation for microgrids,” in AIP Conf. Proc., vol. 3188, AIP Publishing, 2024.
آمار
تعداد مشاهده مقاله: 717
تعداد دریافت فایل اصل مقاله: 494


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب