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Amiri, Farhad, Sadr, Sajad. (1404). Deep Learning- Model Predictive Control for Load Frequency Control of Microgrids with Electric Vehicles. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2025.16193.2250
Farhad Amiri; Sajad Sadr. "Deep Learning- Model Predictive Control for Load Frequency Control of Microgrids with Electric Vehicles". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22098/joape.2025.16193.2250
Amiri, Farhad, Sadr, Sajad. (1404). 'Deep Learning- Model Predictive Control for Load Frequency Control of Microgrids with Electric Vehicles', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2025.16193.2250
Amiri, Farhad, Sadr, Sajad. Deep Learning- Model Predictive Control for Load Frequency Control of Microgrids with Electric Vehicles. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22098/joape.2025.16193.2250

Deep Learning- Model Predictive Control for Load Frequency Control of Microgrids with Electric Vehicles

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 02 آبان 1404    اصل مقاله (2.14 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2025.16193.2250
نویسندگان
Farhad Amiri* ؛ Sajad Sadr
Department of Electrical Engineering, Tafresh University, Tafresh 39518-79611, Iran.
چکیده
In an islanded microgrid, ensuring frequency stability is essential for reliable system operation. Distributed generation (DG) and electric vehicles (EVs) make frequency stability challenging in an islanded microgrid because they increase generation and load variability and reduce system inertia. Load frequency control (LFC) is mainly used to enhance the frequency response of these types of microgrids. In addition, uncertainty in parameters and perturbations strongly impact the application of LFC. To address these challenges, this paper presents an LFC method for islanded microgrids using model predictive control (MPC) based on deep learning. The deep learning technique is used to enhance MPC controller performance against uncertainties and disturbances. The proposed method is validated through experiments, especially in the presence of disturbances and parameter instability. It is then compared with other methods, including linear active disturbance rejection control (LADRC), fractional-order PID (FOPID), and several others. The results show that the MPC method based on deep learning outperforms these approaches in terms of disturbance rejection, frequency response improvement, and system inertia enhancement.
کلیدواژه‌ها
Deep learning؛ electric vehicle؛ islanded microgrid؛ load frequency control
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