NPC type three-level PV grid-connected inverter S-FCS-MPC control strategy

Abstract

Due to constraints from external environmental factors and grid faults, traditional maximum power point tracking (MPPT) and inverter control methods often fail to achieve optimal power output in photovoltaic systems.This can result in misclassification and suboptimal dynamic performance. To address these challenges, we propose a simplified finite set model predictive control (S-FCS-MPC) for grid-connected PV systems. Our system employs a neutral point-clamped (NPC) topology, integrating MPPT and space vector modulation (SVPWM) techniques.Initially, high efficiency is attained by controlling the front stage with an adaptive particle swarm optimization-conductance increment (APSO-INC) approach to accurately identify the maximum power point, while model predictive control (MPC) is used to track the MPP and regulate the boost converter. Subsequently, we simplify the voltage vector for the NPC-type 3-level PV grid-connected inverter, selecting the optimal voltage vector based on the optimal value function, and applying the corresponding switching states to the inverter’s power semiconductors to facilitate grid connection.The proposed system is simulated and evaluated under various dynamic conditions using Matlab/Simulink. The results demonstrate that our algorithm effectively maintains DC-side voltage balance and significantly reduces computational effort compared to traditional methods, confirming its feasibility.