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Machines
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Integration and Implementation of AI in the Control and Monitoring...
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Integration and Implementation of AI in the Control and Monitoring of Electrical Machines, High Power Converters, and Energy Storage 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 May 2024 | Viewed by 962

Special Issue Editor

Dr. Dmitry Baimel

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Shamoon College of Engineering, Beer Sheba, Israel
Interests: energy and power engineering; electric machines and drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the evolving landscape of modern engineering, the integration and implementation of artificial intelligence (AI) have come to represent a force that reshapes industries and drives innovation to unprecedented heights. The field of electrical machines and high-power converters, fundamental to our modern world, is no exception. This Special Issue is dedicated to exploring the influence of AI's integration and application in the control and monitoring of electrical machines, high-power converters, and energy storage systems, reflecting the profound impact these technologies have had on power systems, automation, and industrial sectors.

Electrical machines, high-power converters, and energy storage systems are the backbone of electrical power systems, spanning from renewable energy sources to electric vehicle propulsion and industrial processes. The effective and efficient control and monitoring of these systems are essential for optimizing their performance, ensuring safety, and mitigating operational risks. AI technologies have emerged as game-changers in this domain, offering adaptive, self-learning, and predictive capabilities that enable superior performance, fault detection, and real-time decision-making.

The aim of this Special Issue is to bring together original, theoretical and practical ideas, and future trends of AI implementation in control and monitoring electrical machines (motors and generators), high-power converters (inverters and rectifiers), and energy storage systems. Topics of interest include, but are not limited to, the following:

  • AI implementation for control improvement of motors and generators (all types);
  • AI implementation for control improvement of electric drives;
  • AI implementation for control improvement of multi-level inverters and rectifiers;
  • AI implementation for control improvement of energy storage systems;
  • Fault diagnosis of motors and generators by AI;
  • Fault diagnosis of multi-level inverters and rectifiers by AI;
  • Fault diagnosis of energy storage systems by AI;
  • Application of AI in design processes of electrical machines, drives and energy storage systems;
  • Application of AI for the optimization of propulsion systems of electric vehicles.

Dr. Dmitry Baimel
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. 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

  • artificial intelligence
  • electrical machines
  • fault diagnosis
  • power converters
  • energy storage systems

Published Papers (1 paper)

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Research

18 pages, 6758 KiB  
Article
Advanced Learning Technique Based on Feature Differences of Moving Intervals for Detecting DC Series Arc Failures
by Hoang-Long Dang, Sangshin Kwak and Seungdeog Choi
Machines 2024, 12(3), 167; https://doi.org/10.3390/machines12030167 - 28 Feb 2024
Viewed by 792
Abstract
DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding [...] Read more.
DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems. Full article
(This article belongs to the Special Issue Integration and Implementation of AI in the Control and Monitoring of Electrical Machines, High Power Converters, and Energy Storage Systems)
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