Performance-Based Tuning for a Model Predictive Direct Power Control in a Grid-Tied Converter With L-Filter

Abstract

Model predictive control (MPC) is a powerful and widely used technique to address the control challenges in power converters as the grid interface for renewable energy systems. This technique combines closed-loop control with error and control effort minimization; however, its design is challenging, and we know little about how the controller parameters affect the closed-loop performance of grid-connected voltage source converters. In this study, we applied an MPC direct power control with modulation for a grid-connected power converter with an inductive filter. For the controller design, we proposed an initial set based on the power converter’s nominal setup. Then, we define the range of settings to guarantee stability by analyzing the closed-loop poles of the system. The fine-tuning to improve the performance can be identified visually using the performance maps built from simulations of the control system, simultaneously sweeping the time horizons of the predictive model and the weight factors of the cost function. Experimental results on a low-power bench demonstrate the excellent performance of the designed controller, following and even outperforming the classical proportional-integral (PI) controller and other advanced control techniques.