Model predictive control for a three-phase grid-connected power converters

Abstract

This report provides a complete overview of Model Predictive Control (MPC) approaches used in three-phase grid-connected power converters. With the growing demand for renewable energy integration and efficient power conversion, grid-connected converters play an important role in facilitating seamless energy transfer between renewable energy sources and the power grid. MPC has emerged as a potential control approach for grid-connected converters, providing benefits in terms of performance, robustness, and adaptability. This report examines recent innovations and advancements in MPC approaches designed specifically for three-phase converters, addressing mainly quality improvement. It also tackles important topics such as predictive model design, optimization algorithms, and implementation considerations. This research also investigates the use of sophisticated techniques such as machine learning and adaptive control with MPC to improve performance and adaptability in dynamic grid circumstances. Finally, the report outlines future research paths and prospective topics for additional investigation to enhance the state-of- the-art in MPC for three-phase grid-connected power converters, emphasizing the relevance of robustness and efficiency.