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Voltage Sensorless Model Predictive Control for a Grid-Connected Inverter With LCL Filter
- Source :
- IEEE Transactions on Industrial Electronics. 69:740-751
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- This article introduces a sensorless model predictive control (MPC) scheme for a grid-connected inverter with an inductive–capacitive–inductive ( LCL ) filter using only the grid-side current measurement. A state estimator and a disturbance observer are designed based on the Lyapunov stability theory to reduce the number of sensors and to eliminate the steady-state error. A cost function penalizing the state tracking error is used for the MPC design, and the optimal weights of the cost function are systematically obtained by solving a linear matrix inequality (LMI)-based optimization problem. The variation in grid impedance is taken into account in the LMI optimization. The stability analysis of the overall system is presented, and the frequency responses of the closed-loop and open-loop systems are presented to verify suppression of the filter resonance frequency component. The effectiveness and feasibility of the proposed controller are validated through frequency response analysis and comparative simulation results. Experimental results are also presented to demonstrate the efficacy of the proposed control scheme compared to the linear quadratic regulator method.
- Subjects :
- Lyapunov stability
Optimization problem
Computer science
020208 electrical & electronic engineering
Linear matrix inequality
02 engineering and technology
Linear-quadratic regulator
Filter (signal processing)
Tracking error
Model predictive control
Control and Systems Engineering
Control theory
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Subjects
Details
- ISSN :
- 15579948 and 02780046
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Electronics
- Accession number :
- edsair.doi...........ca5e81743daa22c64a3fa20ea76fa107
- Full Text :
- https://doi.org/10.1109/tie.2021.3050395