A Sampling Time‐Based Comprehensive Analysis of Finite Control Set MPC for the Solar PV‐Fed Quadruple Boost Multilevel Inverter

Model predictive control (MPC) provides notable advantages over conventional linear pulse width modulation (PWM)‐based switching techniques, including faster dynamic response, the ability to manage multiple inputs and outputs simultaneously and the ability to integrate various constraints. Despite these advantages, the use of MPC algorithms in specific multilevel inverter (MLI) topologies remains challenging and underexplored, particularly due to issues such as computational complexity and the need for real‐time processing capabilities. Considering the advantages of MLIs in the solar PV system, it is necessary to analyse the MPC benefits for the same. Therefore, a finite control set model predictive control (FCS‐MPC) algorithm for a nine‐level switched capacitor‐based quadruple‐boost multilevel inverter (QBMLI) is proposed in this paper to exploit the benefits of MPC and MLI in the solar PV system. For this, the system model was initially developed in MATLAB/Simulink, focusing on a PV‐fed QBMLI with an RLE load, which mimics the characteristics of the motor load. Furthermore, a comparative analysis of FCS‐MPC with the linear PI‐based level‐shifted PWM (LSPWM) technique is presented to validate the superior performance of the FCS‐MPC for the considered nine‐level QBMLI. The performance of the system is then validated in real time using a dSPACE Scalexio‐based real‐time hardware‐in‐the‐loop (HIL) setup. Key performance metrics, including load current tracking, transient response and harmonic distortion in load current and voltage, are evaluated under different sampling intervals. In addition, a detailed analysis is carried out to assess the influence of load changes on system performance, providing essential information for the practical application and optimisation of FCS‐MPC.