Advancements in Control and Diagnostics for Electric Motor Drive Systems

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 914

Special Issue Editors


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Guest Editor
Department of Electrical Electronic and Computer Engineering, University of Catania, Catania, Italy
Interests: sensorless control strategies; AC drive control technologies; fault-tolerant motor drives; modeling and control of power converters; advanced technologies for power electronics applications

E-Mail Website
Guest Editor
Department of Electrical Electronic and Computer Engineering, University of Catania, Catania, Italy
Interests: AC drive control technologies; digital signal processing techniques

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovations in control and diagnostics for electric motor drive systems. Motor drives are essential components of modern-day power conversion systems, enabling the efficient and reliable operation of a wide range of applications. The continuous advancements in power electronics, control algorithms, and sensors have paved the way for innovative approaches to motor drive control and diagnostics, leading to improved performance, reliability, and safety of electric machines and drives.

The objective of this Special Issue is to provide a platform for researchers, engineers, and industry experts to share their latest research and developments in the field of motor drive control and diagnostics. This Special Issue aims to cover novel control strategies, sensor technologies, and diagnostic techniques for electric motor drives, along with their practical applications in various industries.

As a reference, the following are some topics of interest for this Special Issue:

  1. Advanced control techniques oriented to maximize the electric drive efficiency;
  2. Diagnostic algorithms for early fault detection and predictive maintenance;
  3. Adaptive and robust control strategies for dynamic motor drive systems;
  4. Sensor fusion and integration for accurate and comprehensive diagnostics;
  5. Advancing intelligent motor control and diagnostics through machine learning and data-driven techniques;
  6. Trends and emerging technologies for motor drive control and diagnostics;
  7. Advanced applications of motor drive control and diagnostics in electric mobility, renewable energy, and marine and aerospace industries.

Dr. Giacomo Scelba
Dr. Luigi Danilo Tornello
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

  • advanced motor drive topologies
  • modulation strategies
  • intelligent control and diagnostics in motor drives
  • high-efficient drives
  • fault detection and fault tolerant motor drives
  • predictive maintenance

Published Papers (1 paper)

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Research

18 pages, 5967 KiB  
Article
Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current
by Laszlo Szamel and Jackson Oloo
Machines 2024, 12(4), 220; https://doi.org/10.3390/machines12040220 - 26 Mar 2024
Viewed by 683
Abstract
Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator [...] Read more.
Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator is composed of windings that are intricately insulated from each other using materials such as enamel wire, polymer films, mica tapes, epoxy resin, varnishes, or insulating tapes. The dielectric strength of the insulation may fail over time due to several environmental factors and processes. Dielectric breakdown of the winding insulation can be caused by rapid switching of the winding current, the presence of contaminants, and thermal aging. For reliable and efficient operation of the SRMs and other electrical machines, it is necessary to take into account the physics of the winding insulation and perform appropriate diagnostics and estimations that can monitor the integrity of the insulation. This article presents the estimation problem using a Genetic Algorithm (GA)-optimized Random Forest Regressor. Empirical properties and measurable quantities in the historical data are utilized to derive temperature and leakage current estimation. The developed model is then combined with a moving average function to increase the accuracy of prediction of the stator winding temperature and leakage current. The performance of the model is compared with that of the Feedforward Neural Network and Long Short-Term Memory over the same winding temperature and leakage current historical data. The performance metrics are based on computation of the Mean Square Error and Mean Absolute Error. Full article
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