Continuous Control Set Model Predictive Control for Grid-Connected Photovoltaic Inverters Under Irradiance Variations

Abstract

This paper proposes a continuous control set model predictive control strategy for a two-stage grid-connected photovoltaic system composed of a boost converter, a three-level neutral-point-clamped inverter, and a grid interface filter of the inductor-capacitor-inductor type. The proposed approach computes optimal continuous voltage references by analytically minimizing a multi-objective cost function, enabling operation with fixed switching frequency and improved power quality. The controller regulates the direct current link voltage, the grid current, and the active and reactive power injected into the grid under dynamic solar irradiance conditions. The system is evaluated in a simulation environment considering irradiance variations of 1000, 700, and 450 watts per square meter. Performance indicators including settling time, total harmonic distortion of the grid current, and root mean square tracking error are analyzed. The results show that the proposed strategy improves current quality, reducing total harmonic distortion to 1.44%, 2.02%, and 2.97%, compared with 2.39%, 3.48%, and 4.97% obtained using a conventional predictive control approach. In addition, the proposed controller improves active power tracking accuracy, reducing the root mean square error from 2498 watts to 1543 watts at nominal irradiance.