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Electric Machinery, Transformers, and Modern Drives—4th Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 April 2026) | Viewed by 1155

Special Issue Editor


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Guest Editor
Department of Electrical & Computer Engineering, Kettering University, Flint, MI 48504, USA
Interests: transformer design; induction motor design; gaseous, solid, and nanocomposite insulating materials; partial discharges in electric machine insulation; inrush current in transformers
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Special Issue Information

Dear Colleagues,

We are pleased to share the success of our Special Issues of Energies, entitled “Electric Machinery and Transformers” , “Electric Machinery and Transformers II”, and “Electric Machinery and Transformers III". 

In the first volume, we successfully published 12 papers: https://www.mdpi.com/journal/energies/special_issues/Electric_Machinery_Transformers

In the second volume, we successfully published 7 papers: https://www.mdpi.com/journal/energies/special_issues/O0245FP2QJ

In the third volume, we successfully published 6 papers: https://www.mdpi.com/journal/energies/special_issues/RY100QC601

We are now preparing to launch the fourth volume of this Special Issue, entitled “Electric Machinery, Transformers, and Modern Drives—4th Edition”.

With rapid developments in materials and semiconductor devices, electric machines and transformers have evolved considerably over the past 10 years and have found use in new applications, such as in e-mobility, aerospace technology, and renewable energy production, where the stringent requirements for a high power density, low weight, compact size, and low cost should be met. Thus, it is a necessity to establish new paradigms to design, construct, and select materials and drive systems for electric machines and transformers. Therefore, the purpose of this Special Issue is to facilitate a platform for disseminating new findings on any aspect of electric machines and transformers.

Topics of interest for publication include, but are not limited to, the following areas:

  • New materials used in electric machines and transformers;
  • Novel designs for electric machines (synchronous motors and generators, brushless DC motors, induction motors, and conventional DC motors) for applications such as drive-by-wire, fly-by-wire, renewable energy production from wind farms, and precision control systems;
  • The design of low-frequency, high-frequency, and pulse transformers for various applications;
  • The development of mathematical models to investigate the performance of electric machines and transformers in a dynamic state as well as in a steady state;
  • Discussions of new methods for design optimization in electric machines and transformers;
  • Novel drive systems to increase the operation performance of electric machines;
  • The prediction of the time to failure of the insulation in large electric machines and transformers;
  • Studies of the thermal behavior of electric machines and transformers for various applications;
  • Acoustic analyses of electric machines and transformers using vibrations;
  • The continuous monitoring of the state of magnetic, as well as insulating, materials in electric machines and transformers during operation.

We welcome your submissions.

Prof. Dr. Huseyin Hiziroglu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • electric machines
  • transformers
  • synchronous machines
  • brushless DC motors
  • induction motors
  • materials for electric machines and transformers

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Related Special Issue

Published Papers (2 papers)

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Research

18 pages, 32550 KB  
Article
Magnetostriction of Silicon Steel Sheets and Its Application in Predicting DC Bias
by Hui Lou, Zhuangzhuang Ding and Kaixing Hong
Energies 2026, 19(9), 2134; https://doi.org/10.3390/en19092134 - 29 Apr 2026
Viewed by 347
Abstract
DC bias is a primary cause of anomalous vibration and noise in power transformers. This study investigates the magnetostriction characteristics of grain-oriented silicon steel sheets under simultaneous AC excitation and DC bias. A novel prediction method is proposed, which integrates multi-scale mutual information [...] Read more.
DC bias is a primary cause of anomalous vibration and noise in power transformers. This study investigates the magnetostriction characteristics of grain-oriented silicon steel sheets under simultaneous AC excitation and DC bias. A novel prediction method is proposed, which integrates multi-scale mutual information features with frequency-domain features, and employs a long short-term memory (LSTM) network for DC bias identification. The experimental platform with six voltage levels and seven bias ratios was set up to collect strain signals under various operating conditions. The results indicate that DC bias alters the magnetostriction spectrum by modulating the nonlinear response. Specifically, the amplitude of the 100 Hz harmonic decreases monotonically as bias increases, whereas the high-frequency harmonics are noticeably amplified, leading to greater waveform asymmetry and harmonic distortion. The proposed prediction model achieves a root-mean-square error (RMSE) of 0.0336 and a coefficient of determination (R2) of 0.8810 under stratified 5-fold cross-validation, offering theoretical support and experimental evidence for DC bias monitoring and transformer condition assessment. Full article
(This article belongs to the Special Issue Electric Machinery, Transformers, and Modern Drives—4th Edition)
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34 pages, 3174 KB  
Article
A Novel Statistical Method for Spectral Analysis of A Short-Duration Signal and Its Application to Current Data for Stator Fault Diagnosis
by Justyna Hebda-Sobkowicz, Anna Michalak, Jacek Wodecki, Radosław Zimroz, Marcin Wolkiewicz and Krzysztof Szabat
Energies 2026, 19(5), 1351; https://doi.org/10.3390/en19051351 - 6 Mar 2026
Viewed by 467
Abstract
In this paper, a novel approach for fault detection in the stator windings of induction motors is presented. The procedure is based on spectral analysis of the current signal. However, due to the specific target application, short duration signals (0.2 s) are utilized, [...] Read more.
In this paper, a novel approach for fault detection in the stator windings of induction motors is presented. The procedure is based on spectral analysis of the current signal. However, due to the specific target application, short duration signals (0.2 s) are utilized, which results in poor spectral resolution. To address this issue, a statistical methodology is developed to minimize uncertainty in decision-making. To construct a health indicator (HI), a statistical analysis is performed to identify spectral components that are both informative and robust. For the selected fault-related frequencies, the HI was created. Using confidence intervals and statistical testing, a fault detection scheme was proposed. The method was validated on an experimental dataset, including both healthy and faulty conditions. The method has been tested on current signals with five levels of fault severity and seven load conditions. Experimental studies on a dedicated test rig demonstrated the high efficiency of the proposed approach for such specific constraints. Full article
(This article belongs to the Special Issue Electric Machinery, Transformers, and Modern Drives—4th Edition)
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