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A hierarchical deep learning approach to optimizing voltage and frequency control in networked microgrid systems

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  • Khosravi, Nima
  • Dowlatabadi, Masrour
  • Sabzevari, Kiomars

Abstract

Distributed energy sources (DERs) and microgrids (MGs) will play an important role in improving the resilience, reliability and sustainability of the grid through dedicated generation, load management and additional capacity struggling to cope with challenges. This study addresses the challenges faced by MG systems, especially in monitoring voltage-frequency operation (V/F) using the proposed two-layer operation scheme that aims to improve MG performance. A pioneering approach is to determine controller coefficients with information from the system components using hierarchical deep-learning-based recurrent convolutional neural network (HDL-RCNN)-excluded attributes have enabled these distributions themselves to determine the optimal conditions for optimal V/F control. Further, the fractional order proportional integral derivative (FOPID) approach, along with the root of the proposed technique, will serve as comparative methods to assess the performance of the HDL-CNN approach. The effectiveness of the proposed method is demonstrated through implementation and validation using the MATLAB/SIMULINK platform.

Suggested Citation

  • Khosravi, Nima & Dowlatabadi, Masrour & Sabzevari, Kiomars, 2025. "A hierarchical deep learning approach to optimizing voltage and frequency control in networked microgrid systems," Applied Energy, Elsevier, vol. 377(PA).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pa:s0306261924016969
    DOI: 10.1016/j.apenergy.2024.124313
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    1. Khosravi, Nima & Oubelaid, Adel, 2025. "Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance," Energy, Elsevier, vol. 320(C).

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