TY - JOUR
T1 - Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control
AU - Akpolat, Alper Nabi
AU - Habibi, Mohammad Reza
AU - Baghaee, Hamid Reza
AU - Dursun, Erkan
AU - Kuzucuoglu, Ahmet Emin
AU - Yang, Yongheng
AU - Dragicevic, Tomislav
AU - Blaabjerg, Frede
N1 - Publisher Copyright:
IEEE
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
KW - Artificial neural network (ANN)
KW - Artificial neural networks
KW - Batteries
KW - battery energy storage system (BESS)
KW - DC microgrids
KW - Energy management
KW - Load modeling
KW - Microgrids
KW - model predictive controller (MPC)
KW - photovoltaics (PVs)
KW - Training
KW - Voltage control
UR - http://www.scopus.com/inward/record.url?scp=85117102873&partnerID=8YFLogxK
U2 - 10.1109/TEC.2021.3118664
DO - 10.1109/TEC.2021.3118664
M3 - Journal article
AN - SCOPUS:85117102873
SN - 0885-8969
VL - 37
SP - 999
EP - 1010
JO - IEEE Transactions on Energy Conversion
JF - IEEE Transactions on Energy Conversion
IS - 2
M1 - 9563239
ER -