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Cyber Physical Fusion-Based Defect Perception, Fault Diagnosis, and Reliability Analytics...
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Cyber Physical Fusion-Based Defect Perception, Fault Diagnosis, and Reliability Analytics in Power Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 1554

Special Issue Editors

Dr. Xian-Bo Wang

E-Mail Website
Guest Editor
Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
Interests: smart grid; wind energy generation and conversion; data-driven fault diagnosis; modeling and optimal control of complex industrial process; fault-tolerant control of real-time systems
Dr. Zhi-Xin Yang

E-Mail Website
Guest Editor
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao SAR 999078, China
Interests: prognostic health monitoring of engineering system; computer vision; robotics and intelligent safety monitoring
Dr. Yunfeng Yan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, China
Interests: computer vision; prognostic health monitoring for power equipment; reliability analytics of power transmission systems

Special Issue Information

Dear Colleagues,

Defect perception, fault diagnosis, and reliability analytics in power systems (e.g., substations, photovoltaic power plants, wind farms, and ultra-high-voltage DC transmission scenarios) based on Cyber Physical Fusion, the Internet of Things (IoT), and Artificial Intelligence (AI) are important topics. However, the IoT and AI are still in their infant stages when it comes to applications for the operation and maintenance of power systems. The implementation of the current IoT and AI-based techniques for power systems lacks a unified reference specification and architecture. Meanwhile, the depth of association and integration between cyber and physical characterization is still not enough. This Special Issue aims to create an international forum for scientists and practicing engineers throughout the world to publish the latest research findings and ideas in mechanism modeling, information-driven diagnosis, reliability analytics, real-time monitoring, and defect detection of power equipment. This Special Issue welcomes theoretical and practical contributions that help us to further understand intelligent techniques, including advanced signal processing, deep learning, fuzzy logic, evolutionary algorithms, swarm intelligence, and interdisciplinary topics. Moreover, this Special Issue also welcomes reports on innovative machines and power systems with applications for the IoT and AI, intelligent health assessment, diagnosis, and control techniques.

This Special Issue seeks to solicit original research articles as well as review articles. Potential topics include, but are not limited to:

  • Advanced digital signal processing methodologies for big data to solve the Prognostic and Health Management (PHM) problem of power equipment;
  • Real-time defect detection and performance evaluation based on physical information for critical components in power generation scenarios;
  • Data-driven health indicator and threshold representation methodologies for fault detection, diagnosis, and isolation;
  • AI-based approaches for fault diagnosis of renewable power generation plants;
  • Advanced fault informative feature (e.g., time-domain analysis and time–frequency domain) representative methods for local defect detection;
  • Spectrum-based capability evaluation of noise disturbance robustness, and weak diagnostic signal enhancement;
  • Applications of AI techniques to imbalanced fault label recognition, and fault diagnosis problems under small sampling data;
  • Big data analysis and processing of the PHM of power equipment combined with Industrial IoT.

Dr. Xian-Bo Wang
Dr. Zhi-Xin Yang
Dr. Yunfeng Yan
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. Electronics 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 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

  • Prognostic and Health Management (PHM)
  • power equipment defect detection
  • Internet of Things
  • reliability analytics of power systems
  • mechanism modeling of power equipment

Published Papers (2 papers)

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Research

14 pages, 3355 KiB  
Article
Dead-Time Effect in Inverters on Wireless Power Transfer
by Vladimir Rajs, Dejana Herceg, Živadin Despotović, Miroslav Bogdanović, Mirjana Šiljegović, Bane Popadić, Zoltan Kiraly, Zoltan Vizvari, Zoltan Sari, Mihaly Klincsik, Imre Felde, Peter Odry and Vladimir Tadic
Electronics 2024, 13(2), 304; https://doi.org/10.3390/electronics13020304 - 10 Jan 2024
Viewed by 766
Abstract
This paper presents a comprehensive analysis of the dead-time effects in wireless power transfer systems based on LCC-S topology. In these systems operating at high frequencies, the ratio of dead-time versus the operating period becomes critical, and the dead-time issue can cause certain [...] Read more.
This paper presents a comprehensive analysis of the dead-time effects in wireless power transfer systems based on LCC-S topology. In these systems operating at high frequencies, the ratio of dead-time versus the operating period becomes critical, and the dead-time issue can cause certain problems regarding power quality, efficiency, and output voltage ripple. The impact of input quantities such as voltage and switching frequency on the efficiency and output power of the LCC-S-tuned WPT system was also investigated. The optimal combination of these parameters used to achieve the maximum efficiency for a target output power and to set the appropriate value of the dead time were determined by running multiple simulations using the MATLAB R2023b software platform. It was also shown that the output voltage remained unchanged with and without a load and up to 1200 ns of dead-time, which provides a simple implementation of the corresponding mathematical model. In the recommended interval of 600–1500 ns, the influence of the dead-time on the value of the output voltage amplitude is less than 10%. The validity of the proposed method was confirmed through the implementation of the experimental prototype, a 5 kW wireless power transmission system, and the obtained results were in accordance with the simulation results. Full article
(This article belongs to the Special Issue Cyber Physical Fusion-Based Defect Perception, Fault Diagnosis, and Reliability Analytics in Power Systems)
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19 pages, 996 KiB  
Article
Uncertainty Detection in Supervisor–Operator Audio Records of Real Electrical Network Operations
by Jaime Acevedo, Gonzalo Garcia, Ricardo Ramirez, Ernesto Fabregas, Gabriel Hermosilla, Sebastián Dormido-Canto and Gonzalo Farias
Electronics 2024, 13(1), 141; https://doi.org/10.3390/electronics13010141 - 28 Dec 2023
Viewed by 551
Abstract
The quality of verbal communication, understood as the absence of uncertainty in the message transmitted, is a key factor in mission-critical processes. Several processes are handled by direct voice communication between these endpoints and any miscommunication could have an impact in success of [...] Read more.
The quality of verbal communication, understood as the absence of uncertainty in the message transmitted, is a key factor in mission-critical processes. Several processes are handled by direct voice communication between these endpoints and any miscommunication could have an impact in success of the task. For that reason, the quality control of verbal communication is required to ensure that the instructions issued are effectively understood and adequately executed. In this context, it is expected that instructions from the command center are issued once, and that the acknowledgment from the field are minimal. In the present work, the communication between an electrical company control center and factory workers in the field was chosen for analysis. We developed two complementary approaches by using machine learning and deep learning algorithms to assess, in an automatic way, the quality of information transmission in the voice communications. Preliminary results demonstrate that the automatic uncertainty detection is feasible, despite the small number of samples available at the present time. To support further studies, a repository was created in GitHub with the spectrogram and the tokenized words of all audios. Full article
(This article belongs to the Special Issue Cyber Physical Fusion-Based Defect Perception, Fault Diagnosis, and Reliability Analytics in Power Systems)
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