Model Predictive Robust Control for A Capacitive-Coupling Grid-Connect Inverter (CGCI) in Power Quality Compensation

In this paper, a model predictive robust control (MPRC) is proposed for a three-phase capacitive-coupling grid-connect inverter (CGCI) to improve the power quality compensation performance under deviating system parameters. A detail stability study and parameter design of the proposed MPRC is provided, while the robustness and power quality compensation performance of the proposed MPRC are verified by the simulation in comparison to the conventional model predictive control (MPC).

Authors

Institutions

Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/iecon55916.2024.10905150
Primary Topic
Multilevel Inverters and Converters
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Model Predictive Robust Control for A Capacitive-Coupling Grid-Connect Inverter (CGCI) in Power Quality Compensation

Chi‐Seng Lam, Pak-Ian Chan, Wai-Kit Sou
Multilevel Inverters and Converters
article

Model Predictive Robust Control for A Capacitive-Coupling Grid-Connect Inverter (CGCI) in Power Quality Compensation

Chi‐Seng Lam, Pak-Ian Chan, Wai-Kit Sou
article en

Abstract

In this paper, a model predictive robust control (MPRC) is proposed for a three-phase capacitive-coupling grid-connect inverter (CGCI) to improve the power quality compensation performance under deviating system parameters. A detail stability study and parameter design of the proposed MPRC is provided, while the robustness and power quality compensation performance of the proposed MPRC are verified by the simulation in comparison to the conventional model predictive control (MPC).

University of Macau (MO)
Technology Development
Affordable and clean energy
Openalex Percentile: Top 74%
Multilevel Inverters and Converters
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.