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Amiri, Farhad. (1403). Designing a robust method to improve virtual inertia control performance in islanded microgrid. نشریه مهندسی عمران امیرکبیر, 57(1), 239-258. doi: 10.22060/eej.2025.23655.5628
Farhad Amiri. "Designing a robust method to improve virtual inertia control performance in islanded microgrid". نشریه مهندسی عمران امیرکبیر, 57, 1, 1403, 239-258. doi: 10.22060/eej.2025.23655.5628
Amiri, Farhad. (1403). 'Designing a robust method to improve virtual inertia control performance in islanded microgrid', نشریه مهندسی عمران امیرکبیر, 57(1), pp. 239-258. doi: 10.22060/eej.2025.23655.5628
Amiri, Farhad. Designing a robust method to improve virtual inertia control performance in islanded microgrid. نشریه مهندسی عمران امیرکبیر, 1403; 57(1): 239-258. doi: 10.22060/eej.2025.23655.5628

Designing a robust method to improve virtual inertia control performance in islanded microgrid

AUT Journal of Electrical Engineering
دوره 57، شماره 1، 2025، صفحه 239-258    اصل مقاله (1.13 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2025.23655.5628
نویسنده
Farhad Amiri*
Department of Electrical Engineering, Tafresh University, Tafresh, Iran,
چکیده
Power-electronic converters play a crucial role in the functioning of microgrids. However, these converters, characterized by their low inertia, present a significant challenge to maintaining a consistent frequency in islanded microgrids. To address this issue, an innovative concept known as virtual inertia control has emerged as a promising solution for enhancing frequency stability in islanded microgrids. The virtual inertia control system does not perform well against disturbances and uncertainty related to microgrid parameters. Therefore, to overcome these problems, it needs a suitable controller in its structure. In this paper, a linear quadratic regulator mode feedback controller based on deep learning is proposed to improve the performance of virtual inertia control in an islanded microgrid against disturbances and uncertainties in the system. The linear quadratic regulator controller uses measurements of system states and the integration of a deep network increases the accuracy and dynamic response of the feedback controller. This allows for fine-tuning of the control response, which exhibits significant robustness against uncertainty in system parameters and disturbances. To evaluate its effectiveness and compare it against alternative control approaches, comprehensive assessments have been conducted across multiple scenarios. The results indicate that the proposed method in the field of virtual inertia control surpasses previous approaches.
کلیدواژه‌ها
Virtual Inertia Control؛ Linear Quadratic Regulator؛ Deep Learning؛ Performance
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