학술논문

Self-Tuning Finite-State Model Predictive Control with Grid Impedance Estimation in a Grid-Tied Inverter
Document Type
Conference
Source
2022 IEEE Energy Conversion Congress and Exposition (ECCE) Energy Conversion Congress and Exposition (ECCE), 2022 IEEE. :1-7 Oct, 2022
Subject
Aerospace
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Adaptation models
Analytical models
Estimation
Automata
Predictive models
Prediction algorithms
Inverters
Model predictive control
three phase inverter
Grid supporting control
LCL filter
Impedance estimation
Language
ISSN
2329-3748
Abstract
This paper presents an embedded finite state Model Predictive Control (MPC) algorithm for a grid connected three phase inverters with LCL-filter. The objective of the MPC is to control the active and reactive power exchanged with the grid. A grid impedance estimation method is proposed and integrated into the finite state MPC using an embedded signal injection. The resulting voltage and current transient are recorded and the Fast Fourier Transform (FFT) is applied to estimate the grid impedance seen at the point of common coupling (PCC). The grid impedance estimation is also used to estimate the grid voltage, allowing to reduce the number of sensors used in LCL-filter. The impedance and grid voltage estimation are then updated in the model predictive controller. The proposed algorithm is tested in simulation and its robustness against changes in grid impedance values will be proved.