Implementation and Comparative Assessment of MPPT Techniques in MPC Based Grid-connected PV System

Due to low conversion losses single-stage conversion process for grid-connected PV systems is getting popular. In recent literature, model predictive control (MPC) is introduced in single-stage PV conversion process since it provides better dynamic response compared to the conventional PI controller. However, the performance of different MPPT techniques is still unknown when MPC control is deployed. This work investigated the performance of adaptive P&O, particle swarm optimization (PSO) and Artificial Bee colony-P&O (ABCPO) algorithm in a MPC- based PV inverter control. The algorithms are tested under both uniform irradiance and partial shading in Matlab Simulink and Opal-RT based hardware setups. It is found that, in a 12S30P PV array, adaptive P&O tracks the global MPP faster compared to the other two. Besides THD induced by Adaptive P&O is significantly lower than PSO and ABCPO. It is concluded that adaptive P&O MPPT algorithms is the most suitable and efficient MPPT algorithm method to be deployed while MPC-based inverter control is implemented in single-stage grid-connected PV system.