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Beta Maximum Power Extraction Operation-Based Model Predictive Current Control for Linear Induction Motors

Authors :
Mohamed. A. Ghalib
Samir A. Hamad
Mahmoud F. Elmorshedy
Dhafer Almakhles
Hazem Hassan Ali
Source :
Journal of Sensor and Actuator Networks, Vol 13, Iss 4, p 37 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

There is an increasing interest in achieving global climate change mitigation targets that target environmental protection. Therefore, electric vehicles (as linear metros) were developed to avoid greenhouse gas emissions, which negatively impact the climate. Hence, this paper proposes a finite set-model predictive-based current control (FS-MPCC) strategy of linear induction motor (LIM) for linear metro drives fed by solar cells with a beta maximum power extraction (B-MPE) control approach to achieve lower thrust ripples and eliminate a selection of weighting factors, the main limitation of conventional model predictive-based thrust control (which can be time consuming and challenging). The B-MPE control approach ensures that the solar cells operate at their maximum power output, maximizing the energy harvested from the sun. Considering a single cost function of primary current errors between the predicted values and their references in αβ-axes, the proposed method eliminates the need for weighting factor selection, thus simplifying the control process. A comparison between the conventional and the presented control method is conducted using MATLAB/Simulink under different scenarios. Comprehensive simulation results of the presented system on a 3 kW LIM prototype revealed that the introduced system based on FS-MPCC surpasses the conventional technique, resulting in a maximum power extraction from solar cells and a suppression of the thrust ripples as well as an avoidance of weighting factor tuning, leading to fewer computational steps.

Details

Language :
English
ISSN :
22242708
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Sensor and Actuator Networks
Publication Type :
Academic Journal
Accession number :
edsdoj.80377757cbb24c07a80579102c2bc32d
Document Type :
article
Full Text :
https://doi.org/10.3390/jsan13040037