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

Over the past decade, 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 establishment and control. At same time, they offer higher efficiency reliability compared their alternating (AC) counterparts. This paper proposes new model predictive control (MPC)-trained artificial neural network (ANN) strategy an ANN-MPC instead conventional cascaded-proportional-integral (PI)-trained ANN dynamic damping photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, proposed approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) output (load demand), hence achieving quick DC-bus voltage damping. The scheme is examined different operating conditions, results with ANN-based PI controller. show strategy's efficacy lessen instability issues achieve effective attenuation oscillations