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Advances in Monitoring and Fault Diagnosis for Power Equipment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 703

Special Issue Editors


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Guest Editor
State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
Interests: intelligent design of power equipment; wireless energy transmission technology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: AI-driven plasma modeling; plasma basic data calculation; reduced order modeling and digital twin; eco-friendly gases for insulation and arc quenching; computer vision and federated learning for power maintenance

Special Issue Information

Dear Colleagues,

The reliable and efficient operation of power equipment is paramount to ensuring the stability and security of modern power systems. However, power equipment is constantly subjected to various stresses during operation, leading to potential failures that can result in significant economic losses and safety hazards. Therefore, developing advanced monitoring and fault diagnosis techniques is crucial for the early detection, accurate identification, and timely mitigation of potential faults in power equipment.

This Special Issue, "Advances in Monitoring and Fault Diagnosis for Power Equipment", aims to provide a platform for researchers and engineers to share their latest findings and innovations in this critical field. We seek high-quality original research articles and comprehensive reviews that address the challenges and opportunities in advanced sensing technologies, signal processing and feature extraction, fault diagnosis and prediction models, condition monitoring and health management, and applications in various power equipment.

This Special Issue will contribute to the advancement of knowledge and technology in power equipment monitoring and fault diagnosis, ultimately leading to the improved reliability, safety, and efficiency of power systems. We believe that the published research will be of great interest to a wide range of readers, including researchers, engineers, and practitioners in the field of power systems and related disciplines.

Dr. Huan Yuan
Dr. Linlin Zhong
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. Applied Sciences 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

  • power equipment
  • condition monitoring
  • fault diagnosis
  • signal processing
  • machine learning
  • artificial intelligence
  • predictive maintenanc
  • power system reliability

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Published Papers (1 paper)

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Research

19 pages, 4782 KB  
Article
PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
by Weifeng Xin, Wei Song, Manling Dong, Xiaochuan Huang, Xiaoshi Kou, Zhenyu Zhan, Xinyue Shi and Xutao Han
Appl. Sci. 2025, 15(14), 8113; https://doi.org/10.3390/app15148113 - 21 Jul 2025
Viewed by 409
Abstract
Defects in GIS can be effectively detected by detecting the partial discharge (PD). The common methods of detecting partial discharge are pulse current, ultrasonic and UHF (ultra-high frequency). However, the results of different methods may be different due to the different physical quantities [...] Read more.
Defects in GIS can be effectively detected by detecting the partial discharge (PD). The common methods of detecting partial discharge are pulse current, ultrasonic and UHF (ultra-high frequency). However, the results of different methods may be different due to the different physical quantities detected. It is important to research the differences between the PD detection methods for the PD detection and analysis. In this study, we designed metal protrusion defects in GIS, including protrusion on the conductor and enclosure. Then, we detected the PD of defects using pulse current, UHF and ultrasonic methods at the same time. The PRPD patterns, maximum discharge amplitude of different defects and PD inception voltage (PDIV) detected by the three methods were analyzed. The PRPD patterns and discharge amplitude of the different methods were very similar to each other, but the PDIVs were different. It can be concluded that the process from the PD inception to breakdown can be divided into four sections based on the PRPD and the maximum discharge amplitude. The similarity between the three methods is because their signals are all related to the pulse current during the PD process, and differences in their PDIVs are caused by the differences in sensitivity. The sensitivity of the pulse current is the lowest among the three methods due to its poor anti-jamming capability. The sensitivity of UHF is higher, and that of ultrasonic is the highest. Full article
(This article belongs to the Special Issue Advances in Monitoring and Fault Diagnosis for Power Equipment)
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