Modern Control and Diagnosis for Electrical Machines and Drives
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: 10 December 2025 | Viewed by 19
Special Issue Editors
Interests: electric drive design and embedded diagnosis techniques for multiphase machines; electric vehicles; renewable energy conversion systems
Special Issues, Collections and Topics in MDPI journals
Interests: power electronics and drives for electric vehicles; renewable energy systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent decades, a substantial number of research studies have concentrated on new control strategies for and condition monitoring of electrical machines and drives. Accurately diagnosing and promptly identifying incipient faults can significantly lower maintenance costs and minimize downtime in varied industry applications. As industrial systems evolve toward higher efficiency, automation, and reliability, the importance of fault detection and control strategies becomes increasingly critical.
This Special Issue focuses on innovative control strategies, fault analysis, condition monitoring, fault detection, and fault-tolerant techniques for electrical machines and drives. It will bring together cutting-edge research that addresses the growing challenges in ensuring system reliability and performance under faulty conditions.
Topics of interest include, but are not limited to, the following:
- Control strategies, fault development analysis, and modeling for electrical machines, including induction motor faults, permanent magnet synchronous machine faults, multiphase machine faults, etc.;
- Power electronics, including fault-tolerant inverters, modular multilevel converters, IGBTs, SiC MOSFET power modules, and electrostatic discharge testing;
- Specialized signal processing techniques for fault detection and quantification, including time-domain techniques, frequency-domain techniques, time-frequency domain techniques, etc.;
- AI techniques for fault detection and classification: machine learning algorithms (support vector machines, random forests, k-nearest neighbors, and decision trees), and deep learning models (convolutional neural networks, recurrent neural networks);
- Integrating signal processing and AI techniques for feature extraction and hybrid model development.
Dr. Yasser Gritli
Dr. Claudio Rossi
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. Energies 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 2600 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
- fault detection
- fault diagnosis
- fault-tolerant control
- signal processing techniques
- AI techniques
- multiphase machines
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