IEEE Transactions On Energy Conversion, cilt.1, sa.1, ss.1-12, 2021 (SCI-Expanded)
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.