Bias-Free Predictive Control of Power Converters with LCL Filter in Micro-Energy Systems

Abstract

Power converters with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filters have been widely adopted in systems, e.g., micro-energy systems, grid-tied renewable energy systems, etc. Such a system forms a high-order <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple input and multiple output</i> (MIMO) dynamic system, requiring complicated active damping and showing slow control dynamics, applying classical cascaded linear controllers. Model predictive control (MPC) is a powerful control strategy, which inherently suits for MIMO systems with constraints. However, for such systems, inevitable tracking biases are seen when using the classical MPC. In addition, the nonlinear nature of the underlying system leads to difficulty for a deep analysis of an MPC controller design. In this work, we mathematically reveal the cause of tracking biases when applying classical MPC, and develop an equivalent modeling method to eliminate it at both parameter and model uncertainties, forming a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robust and bias-free MPC</i> . The proposed solution remains fast in control dynamics and simple in structure. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Both</i> simulation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> experimental data confirm the effectiveness of the proposed solution in mitigating tracking bias and good robustness at model deviations and grid disturbances.