Dynamic Stabilization of DC Microgrids using ANN-Based Model Predictive Control

Alper Nabi Akpolat, Mohammad Reza Habibi, Hamid Reza Baghaee, Erkan Dursun, Ahmet Emin Emin Kuzucuoglu, Yongheng Yang, Tomislav Dragicevic, Frede Blaabjerg

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    Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.
    Original languageEnglish
    JournalIEEE Transactions on Energy Conversion
    Issue number2
    Pages (from-to)999-1010
    Publication statusPublished - 2022


    • Artificial neural network (ANN)
    • Artificial neural networks
    • Batteries
    • Battery energy storage system (BESS)
    • DC microgrids
    • Energy management
    • Load modeling
    • Microgrids
    • Model predictive controller (MPC)
    • Photovoltaics (PVs)
    • Training
    • Voltage control


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