A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

The fuel cell has been regarded as one of the most promising renewable energy technologies for various applications such as distributed power generation, transportation, portable power source, and automobile. The output power of a fuel cell is affected by operational parameters such as cell temperature, oxygen partial pressure, and hydrogen partial pressure. This paper deal with a two-stage grid-connected PEMFC system. In the first stage, a fuzzy logic controller is proposed to track the maximum power generated by PEMFC by using a boost converter. In the second stage, a multi-objective FSC-MPC two-step prediction to control of 3L-NPC is proposed. The use of Finite Control Set Model Predictive Control (FSC-MPC) can significantly improve the control performance of a three-level NPC (3L-NPC) inverter. Furthermore, this method uses the inverter's discrete behavior to find optimal switching states that minimize the cost function. The suggested model predictive control method for the 3L-NPC inverter is based on a multi-objective cost function that is meant to regulate inverter currents, dc-link voltage balance, and minimize the number of switch states. The performance of the proposed MO–FSC–MPCTS controlled 3L-NPC inverter is simulated with MATLAB/Simulink. The results show that the suggested method ensures MPP tracking and injecting the current into the grid with a 2% THD.