Relegated Thrust Ripples for Linear Induction Motors Based-Four Voltage Vectors Finite-Set Predictive Control and Model Reference Adaptive System

Abstract

—These days, there are extensive signifies to the evolution of newfound control procedures like model predictive control. The finite-state predictive thrust control (FSPTC) for LIMs is still not deftly addressed by scholars and limited research was acted to exploit the FSPTC for LIM drive systems. Therefore, the current study proposes a novel finite-state predictive thrust control (NFSPTC) for linear induction motors (LIMs) with reduced numbers of voltage vectors (VVs) in the prediction phase. The NFSPTC utilizes the conventional direct thrust control (DTC) concept to reduce the required VVs from eight to only four which consist of two active vectors and two zero vectors. Decreasing the number of predicted VVs leads to a significant reduction in computational time. Moreover, the weighting factor is removed to reduce the effort in selecting the best weighting factor. Since the LIM drive system can be succeeded by developing a sensorless speed estimation, the linear speed is estimated depending on the model reference adaptive system (MRAS) before using actual sensors; thus, low cost can be attained. The suggested control approach is validated by simulation proofs based on a 3-kW arc machine. The gained findings clarified the effectiveness of the proposed four VVs control over the eight VVs procedure in dipping the thrust ripple. Besides, using the four VVs control under variable speed and fixed thrust load, the execution time was decreased by 50%. Furthermore, the proposed control procedures ensued in maintaining the primary flux linkage constant alongside succeeding a glowing-tracking speed profile throughout the tackled speed and thrust load variations.”

Keywords:

• Finite state predictive thrust control
• model reference adaptive system
• direct thrust control
• sensor-less control

ACKNOWLEDGEMENT

This work was supported by the research grants [PID-000085-01-04], Prince Sultan Defense Studies & Research Center, Saudi Arabia. The authors would also like to acknowledge the technical and support received from Renewable Energy Lab, Prince Sultan University, Saudi Arabia.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mahmoud F. Elmorshedy

Mahmoud F. Elmorshedy (M’19-SM’23) was born in Gharbeya, Egypt, in 1989. He received the B.Sc. and M.Sc. degrees in electrical engineering from Tanta University, Egypt, in 2012 and 2016, respectively, and the Ph.D. degree in electrical engineering from the School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, China, in 2020. He started working as a Teaching Assistant with the Department of Electrical Power and Machines Engineering, Faculty of Engineering, Tanta University, in 2013, where he was promoted to Assistant Lecturer, in June 2016. He is currently working as an Assistant Professor (On academic leave) with the Department of Electrical Power and Machines Engineering, Faculty of Engineering, Tanta University. His current job is as a Postdoctoral Fellow with the Renewable Energy Laboratory, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia. His research interests include linear induction motor, predictive control, power electronics, and renewable energy.

M. S. Bhaskar

M. S. Bhaskar (M’15-SM’20) received the Ph.D. in Electrical and Electronic Engineering, University of Johannesburg, South Africa in 2019. He is with Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia. He was a Post-Doctoral researcher with his Ph.D. tutor in the Department of Energy Technology, Aalborg University, Esbjerg, Denmark in 2019. He is an Associate Editor of IET Power Electronics (UK), IET The Journal of Engineering (UK), Springer- Green Technology, Resilience, and Sustainability, River Publisher- Distributed generation and Alternative energy, Journal of Power and Energy Engg., Scientific Research and Topic Editor of MDPI Electronics, Switzerland.

Dhafer Almakhles

Dhafer Almakhles (Senior Member, IEEE) received the Ph.D. degrees from The University of Auckland, New Zealand, in 2011 and 2016, respectively. Since 2016, he has been with Prince Sultan University, Saudi Arabia, where he is currently the Chairperson of the Communications and Networks Engineering Department and the Director of the Science and Technology Unit. He is also the Leader of the Renewable Energy Research Team and the Laboratory at Prince Sultan University. His research interests include the hardware implementation of control theory, signal processing, networked control systems, and sliding mode.

Kotb M. Kotb

Kotb M. Kotb received the B.Sc. and M.Sc. degrees in electrical engineering from Tanta University, Egypt, in 2012 and 2017, respectively. He obtained his Ph.D. degree from Budapest University of Technology and Economics, Budapest, Hungary in 2022. In October 2022, he has been promoted to Assistant Professor at the Department of Electrical Power and Machines Engineering, Faculty of Engineering, Tanta University, Egypt. In late 2023, he joined the Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management (IRC-HTCM), King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. His research interests are directed to renewable energy systems, planning optimization, energy storage systems, microgrids, net-zero emissions systems, Hydrogen energy, and power electronics.