PLPR-Based Predictive Control for <i>LCL</i>-Filtered Voltage Source Inverters
Zheng Yin, Fujin Deng, Abdelhady Ghanem, Sahar S. Kaddah, Sayed Abulanwar
Abstract
The model predictive control (MPC) is sensitive to parameter variations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -filtered voltage source inverters (VSIs). To improve the robustness of the MPC, this article proposes a parameter less prediction and reference-based predictive control (PLPR-PC) for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -filtered VSIs, where the proposed PLPR-PC not only obtains the parameter less predictions by measuring and updating all current-voltage gradients, but also obtains the parameter less references by using the phase differences between measured currents and voltages. The proposed PLPR-PC effectively eliminates parametric effects on both prediction and reference calculations and thereby improve the robustness of grid current for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -filtered VSIs under parameter variations. The 30-kW simulation and experimental prototype are built up to verify the feasibility and performance of proposed PLPR-PC.