Artificial Intelligence-Based Weighting Factor Autotuning for Model Predictive Control of Grid-Tied Packed U-Cell Inverter

Mostefa Mohamed-Seghir, Abdelbasset Krama, Shady S. Refaat, Mohamed Trabelsi and Haitham Abu-Rub
Additional contact information
Mostefa Mohamed-Seghir: Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland
Abdelbasset Krama: LEVRES Laboratory, The University of El-Oued, Fac. Technology, El-Oued 39000, Algeria
Shady S. Refaat: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar
Mohamed Trabelsi: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar
Haitham Abu-Rub: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar

Energies, 2020, vol. 13, issue 12, 1-14

Abstract: The tuning of weighting factor has been considered as the most challenging task in the implementation of multi-objective model predictive control (MPC) techniques. Thus, this paper proposes an artificial intelligence (AI)-based weighting factor autotuning in the design of a finite control set MPC (FCS-MPC) applied to a grid-tied seven-level packed U-cell (PUC7) multilevel inverter (MLI). The studied topology is capable of producing a seven-level output voltage waveform and inject sinusoidal current to the grid with high power quality while using a reduced number of components. The proposed cost function optimization algorithm ensures auto-adjustment of the weighting factor to guarantee low injected grid current total harmonic distortion (THD) at different power ratings while balancing the capacitor voltage. The optimal weighting factor value is selected at each sampling time to guarantee a stable operation of the PUC inverter with high power quality. The weighting factor selection is performed using an artificial neural network (ANN) based on the measured injected grid current. Simulation and experimental results are presented to show the high performance of the proposed strategy in handling multi-objective control problems.

Keywords: artificial intelligence; packed U-cell (PUC) inverter; weighting factor autotuning; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/12/3107/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/12/3107/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:12:p:3107-:d:372144

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().