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

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 (F...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2020-06, Vol.13 (12), p.3107
Main Authors: Mohamed-Seghir, Mostefa, Krama, Abdelbasset, Refaat, Shady S., Trabelsi, Mohamed, Abu-Rub, Haitham
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer simulation
Cost function
Electric potential
Genetic algorithms
Harmonic distortion
Inverters
model predictive control
Multiple objective analysis
Neural networks
Optimization
Optimization techniques
packed U-cell (PUC) inverter
Power
Simulation
Topology
Variables
Voltage
Waveforms
Weighting
weighting factor autotuning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13123107