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Machines
Special Issues
Advanced Control of Electric Machines and Sustainable Energy Systems
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Advanced Control of Electric Machines and Sustainable Energy Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 9120

Special Issue Editors

Dr. Mohamad Alzayed

E-Mail Website
Guest Editor
1. Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
2. Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Interests: control systems; power systems; power electronics; electric machines; smart grid; renewable and distributed energy resources; power quality and energy management; real-time simulations
Special Issues, Collections and Topics in MDPI journals
Dr. Hicham Chaoui

E-Mail Website
Guest Editor
1. Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
2. Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
Interests: multi-objective optimization; energy/health-conscious mechatronics; robotics; autonomous systems; diagnosis/prognosis; electrified transportation; motor drives; energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical machines play essential roles in all aspects of our lives, from home appliances to industries and renewable energy resources. Novel, advanced control strategies have contributed to the improvements in the performance of electrical machines and their applications, such as in motors, electric vehicles, and power devices, as well as in sustainable energy system approaches. New techniques have also emerged to control electrical machines, including artificial intelligence, neural networks, fuzzy logic, neuro-fuzzy logic, evolutionary computation, self-organizing systems, machine learning, multiagent systems, the internet of things, and big data analysis, to name a few. This Special Issue focuses on the advances in control strategies related to electrical machines and sustainable energy systems, such as new technologies including IoT, artificial intelligence, new applications, etc., as well as their effective solutions proven through analysis, simulation, and/or experiments.

Authors are invited to submit full papers describing original research work in related areas, including, but not limited to, the following:

  • New system architectures and technologies.
  • Applications of electrical machines.
  • Modeling and control of electrical machines and sustainable energy systems.
  • Advanced control of power devices and systems.
  • Advanced control and optimization algorithms for electrical power systems.
  • Application of IoT and artificial intelligence in electric machines and sustainable energy systems.
  • Techniques using neural networks, fuzzy logic, neuro-fuzzy logic, and machine learning for electric machines and sustainable energy systems.
  • Control methods of motor drives, including all the aspects of control problems for motor drives (controller design, observer design, fault-tolerant control, sensorless control, etc.).
  • Evaluation of electric machine losses and efficiency.
  • Advanced power-electronic converter topologies for electric machines and sustainable energy systems.
  • Modern fault diagnosis methods for electric machine systems and sustainable energy systems.

Dr. Mohamad Alzayed
Dr. Hicham Chaoui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrical machines and systems
  • sustainable energy systems
  • novel design
  • advanced control
  • internet of things
  • artificial intelligence
  • electric vehicles
  • power devices
  • power systems
  • stability analysis

Published Papers (2 papers)

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Research

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18 pages, 8762 KiB  
Article
Coupled Electromagnetic–Fluid–Thermal Analysis of a Fully Air-Cooled Pumped Storage Generator Motor
by Shukuan Zhang, Fachen Wang, Hongtao Wang, Zhe Shao, Hongge Zhao and Jingwei Zhu
Machines 2023, 11(9), 901; https://doi.org/10.3390/machines11090901 - 10 Sep 2023
Viewed by 769
Abstract
With the continuous increase in the capacity of the pumped storage generator motor, the overheating of the rotor area is becoming increasingly severe, which has a significant effect on the safe and reliable operation of the machine. The heat dissipation of the machine [...] Read more.
With the continuous increase in the capacity of the pumped storage generator motor, the overheating of the rotor area is becoming increasingly severe, which has a significant effect on the safe and reliable operation of the machine. The heat dissipation of the machine rotor by fully air-cooled is one of the key technologies to develop the new generation of pumped storage generator motors. In this paper, the electromagnetic field and fluid–thermal coupled field of a pumped storage generator motor are analyzed. The 2D transient time-step finite element model of the electromagnetic field of a pumped storage generator motor is established, and the eddy current loss of damping bars of the rotor is calculated by the finite element method. The additional loss of the rotor pole surface is calculated by analytical method. The mathematical and geometric models of the 3D fluid–thermal coupled field of the pumped storage generator motor are established and calculated. The complex fluid velocity distribution and the temperature distribution at different positions of the rotor under fully air-cooled fanless cooling conditions are investigated in detail. The calculated temperature of field winding is compared with the measured value, and the result shows that the calculated result coincident well with the test data. This research provides the technical reference for the development and temperature rise calculation for large pumped storage generator motors. Full article
(This article belongs to the Special Issue Advanced Control of Electric Machines and Sustainable Energy Systems)
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Review

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33 pages, 4851 KiB  
Review
Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles
by Mohammad Zamani Khaneghah, Mohamad Alzayed and Hicham Chaoui
Machines 2023, 11(7), 713; https://doi.org/10.3390/machines11070713 - 05 Jul 2023
Cited by 6 | Viewed by 7784
Abstract
Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types [...] Read more.
Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field. Full article
(This article belongs to the Special Issue Advanced Control of Electric Machines and Sustainable Energy Systems)
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