Model Predictive Control for Nonideal Positive Output Superlift Luo DC-DC Converter

Abstract

Photovoltaic application as a renewable energy source demands stable and efficient power conversion techniques, especially under fluctuating input conditions due to solar power generation. Positive Output Superlift Luo Converter has a high voltage gain but has a nonlinear characteristic from its parasitic components. Nevertheless, common control method is not effective to overcome the nonideality of the converter. In this study, we propose a Model Predictive Control (MPC) strategy for a nonideal POSLLC, with a model derived from a discrete-time state-space model using the state-space averaging method. Simulation results showed that the MPC strategy improves the performance of the nonideal converter compared to an open-loop operation. The output voltage overshoot was reduced from 8.03% to 4.55%, and the settling time was shortened from 7.55 to 6.9 ms. As a result, the MPC strategy provides better damping and faster response to abrupt change in input voltage. The results demonstrate that MPC provides precise voltage regulation and adaptability for nonideal high-gain converters in PV-based power systems.