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Feature Review Papers in “Electrical, Electronics, and Communications Engineering” Section
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Feature Review Papers in “Electrical, Electronics, and Communications Engineering” Section

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1252

Special Issue Editor

Prof. Dr. Mohamed Benbouzid

E-Mail Website
Guest Editor
Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Interests: fault detection and diagnosis; failure prognosis; cyberattack detection; fault-resilient control; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to feature papers highlighting recent advances in the electrical, electronic, and communications engineering section (https://www.mdpi.com/journal/applsci/sections/electrical_electronics_communications_engineering) for relevant topics.

Focusing on emerging techniques and applications, review papers are welcome, covering experimental, theoretical, or hybrid approaches. Key topics include electrical engineering (such as electrical machines and drives, fault diagnosis and prognosis, fault-tolerant or resilient control, power quality, smart grids, microgrids, nanogrids, and renewable energy harvesting), electronics engineering (including antennas and radio propagation, electromagnetic compatibility, microwaves, radar, and sonar navigation), and communications engineering (encompassing connected vehicles, the IoT, networking technologies, and wireless networks).

These featured review papers are expected to be read by a large number of researchers while being highly influential. In addition, all papers presented in this Special Issue will be published in a printed edition, which will be widely promoted within the scientific community

Prof. Dr. Mohamed Benbouzid
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • tidal and wave power
  • wind power
  • microgrids
  • energy management
  • fault detection and diagnosis
  • fault-tolerant control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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31 pages, 11302 KiB  
Article
Leveraging Digital Twins and AI for Enhanced Gearbox Condition Monitoring in Wind Turbines: A Review
by Houssem Habbouche, Yassine Amirat and Mohamed Benbouzid
Appl. Sci. 2025, 15(10), 5725; https://doi.org/10.3390/app15105725 - 20 May 2025
Abstract
Wind power plays a significant role in sustainable energy production, but the reliability of wind turbines depends heavily on the integrity of their gearboxes. Gearbox failures can lead to significant downtime and operational disruption. In this context, this paper provides an overview of [...] Read more.
Wind power plays a significant role in sustainable energy production, but the reliability of wind turbines depends heavily on the integrity of their gearboxes. Gearbox failures can lead to significant downtime and operational disruption. In this context, this paper provides an overview of the evolution of gearbox monitoring techniques, culminating in the emergence of digital twin (DT) technology. We explore the application of DT technology to gearbox condition monitoring, focusing on two critical components: bearings and gears. This includes a comprehensive review of methodologies involving model-based approaches and data-driven techniques using signal processing (SP) and artificial intelligence (AI) algorithms. We address the challenges of “learning with minimal knowledge” and propose a framework for the effective application of DT technology. Finally, we discuss future research directions and potential contributions to advancing the field of gearbox condition monitoring through the continued development and implementation of DT-based solutions. Full article
(This article belongs to the Special Issue Feature Review Papers in “Electrical, Electronics, and Communications Engineering” Section)
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Review

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39 pages, 4276 KiB  
Review
A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
by Cosmina-Mihaela Rosca and Adrian Stancu
Appl. Sci. 2025, 15(7), 3758; https://doi.org/10.3390/app15073758 - 29 Mar 2025
Cited by 1 | Viewed by 766
Abstract
Wind energy represents a solution for reducing environmental impact. For this reason, this research studies the elements that propose optimizing wind energy production through intelligent solutions. Although there are studies that address the optimization of turbine performance or other indirectly related factors in [...] Read more.
Wind energy represents a solution for reducing environmental impact. For this reason, this research studies the elements that propose optimizing wind energy production through intelligent solutions. Although there are studies that address the optimization of turbine performance or other indirectly related factors in wind energy production, the optimization of wind energy production remains a topic insufficiently explored and synthesized in the literature. This research studies how machine learning (ML) techniques can be applied to optimize wind energy production. This research aims to study the systematic applications of ML to identify and analyze the key stages of optimized wind energy production. Through this research, case studies are highlighted by which ML methods are proposed that directly target the issue of optimizing the wind power process through wind turbines. From the total of 1049 articles obtained from the Web of Science database, the most studied ML models in the context of wind energy are the artificial neural networks, with 478 papers identified. Additionally, the literature identifies 224 articles that have studied random forest and 114 that have incorporated gradient boosting about wind power. Among these, 60 articles have specifically addressed the issue of optimizing wind energy production. This aspect allows for the identification of gaps in the literature. The research notes that previous studies have focused on wind forecasting, fault detection, or turbine efficiency. The existing literature addresses the indirect optimization of component performance. Thus, this paper identifies gaps in the current research, discusses ML algorithms in the context of optimizing wind energy production processes, and identifies future directions for increasing the efficiency of wind turbines through integrated predictive methods. Full article
(This article belongs to the Special Issue Feature Review Papers in “Electrical, Electronics, and Communications Engineering” Section)
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