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Machines
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Fault Tolerant Control of Induction Motor
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Fault Tolerant Control of Induction Motor

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3807

Special Issue Editor

Dr. Vanesa Fernández-Cavero

E-Mail Website
Guest Editor
Electrical Engineering Area, Department of Electromechanical Engineering, University of Burgos, 09001 Burgos, Spain
Interests: induction motor; fault detection; fault diagnosis; electrical machines

Special Issue Information

Dear Colleagues,

Induction motors are the most prevalent element across the industrial sector, being key to all kinds of processes. Although they are known to be robust and resistant element, the stresses to which they are subjected can nevertheless lead to failures.

These failures involve important costs for the industries, necessitating their detection early on, as well as their correct identification. Therefore, the study and control of induction motor failures is a subject of vital interest.

This Special Issue focuses on the control of these faults, applied specifically to induction motors. Innovative papers that delve deeper into this study are therefore welcome.

Dr. Vanesa Fernández-Cavero
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

  • fault detection techniques
  • condition monitoring of electrical machines
  • online monitoring and diagnosis
  • classifiers for fault diagnosis
  • correlation Techniques
  • diagnostics faults analysis
  • preventive faults analysis

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Published Papers (1 paper)

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Research

17 pages, 1653 KiB  
Article
Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology
by Lei Song, Chunguang Lu, Chen Li, Yongjin Xu, Jiangming Zhang, Lin Liu, Wei Liu and Xianbo Wang
Machines 2024, 12(2), 134; https://doi.org/10.3390/machines12020134 - 14 Feb 2024
Cited by 2 | Viewed by 2832
Abstract
With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. [...] Read more.
With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model. Full article
(This article belongs to the Special Issue Fault Tolerant Control of Induction Motor)
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