Manara - Qatar Research Repository
Browse

Reconfigurable Model Predictive Control for Grid Connected PV Systems Using Thirteen-Level Packed E-Cell Inverter

Download (4.07 MB)
journal contribution
submitted on 2023-08-31, 05:53 and posted on 2023-08-31, 11:19 authored by Abdelbaset Laib, Abdelbasset Krama, Abdeslem Sahli, Abbes Kihal, Haitham Abu-Rub

This paper describes a novel single-phase thirteen-level packed E-cell inverter (PEC13) for grid-tied photovoltaic (PV) systems. PEC13 topology contains six power switches, two four-quadrant switches and four DC capacitors. The main advantage offered by the proposed PEC13 is the ability to produce different voltage levels without any modifications in power circuit when open circuit fault occurs on one or two four-quadrant switches. In faulty condition, PEC13 can continue its operation and behaves as PEC9 or PUC7 inverter configuration. Furthermore, a reconfigurable finite-control set model predictive control (R-FCS-MPC) is designed to control the proposed grid-connected PV systems based PEC13 inverter. The proposed R-FCS-MPC assures the transition between PEC13 to PEC9 or PUC7 configuration through the adjustment of DC-link capacitor voltages balancing. The effectiveness of the R-FCS-MPC with the proposed PEC13 topology is evaluated through experimental validations using real-time hardware in the loop (HIL) setup. Provided experimental results demonstrate that the R-FCS-MPC enables to achieve the normal operation, multi-functionality, besides the high performance of the novel PEC13 inverter topology under step change in solar irradiance and faulty operation of four-quadrant switches.

Other Information

Published in: IEEE Access
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1109/access.2022.3208106

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Texas A&M University at Qatar