Journal article
Dynamic Stabilization of DC Microgrids using ANN-Based Model Predictive Control
Marmara University1
Aalborg University2
Amirkabir University of Technology3
Zhejiang University4
Department of Electrical Engineering, Technical University of Denmark5
Electronics, Department of Electrical Engineering, Technical University of Denmark6
Center for Electric Power and Energy, Centers, Technical University of Denmark7
Smart Electric Components, Center for Electric Power and Energy, Centers, Technical University of Denmark8
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.
Language: | English |
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Year: | 2022 |
Pages: | 999-1010 |
ISSN: | 15580059 and 08858969 |
Types: | Journal article |
DOI: | 10.1109/TEC.2021.3118664 |
ORCIDs: | Dragicevic, Tomislav |