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Testing, Monitoring and Diagnostic of High Voltage Equipment, 3rd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F6: High Voltage".

Deadline for manuscript submissions: 10 April 2026 | Viewed by 988

Special Issue Editors

Special Issue Information

Dear Colleagues,

Techniques for the transmission of electrical energy have been developed since the first stumbling steps in the late 19th century, leading into today’s sprawling international grid providing electricity in virtually any nook and cranny of the world. Increasing demand has necessitated increase of transmission voltages. Therefore, high voltage equipment has become an increasingly important part of the power system. Testing, monitoring and diagnostic on high voltage equipment (Insulator, transformer, cable, GIS, etc.) are the base of condition maintenance and essential for an economic usage of high voltage equipment.

This Special Issue is aimed at exploring and demonstrating the novel ideas and valuable outcomes addressing the various aspects of testing, monitoring, and diagnostic of high voltage equipment, such as

  • On-line monitoring technology
  • Partial discharge measurement
  • New methods for testing, monitoring, and diagnostic
  • Pollution and icing characteristics of insulation equipment
  • Insulation deterioration characteristics and diagnosis (Power cable, silicone rubber composite insulator, etc.)
  • Electrical breakdown characteristics
  • Numerical modeling and simulation

While the above-mentioned list may not cover all the issues related to testing, monitoring, and diagnostics of high voltage equipment, this Special Issue welcomes the submission of manuscripts on other similar topics of interest as well as those that contribute to enhance the knowledge, testing methods, condition monitoring abilities, diagnostic methods, and thus the reliability of the power grids.

Prof. Dr. Zhijin Zhang
Dr. Hualong Zheng
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 250 words) can be sent to the Editorial Office for assessment.

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. Energies 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 2600 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

  • high-voltage tests
  • measurement techniques
  • novel measurement devices
  • high voltage operation
  • electrical insulation
  • condition monitoring
  • insulation Breakdown
  • partial discharge
  • aging performance

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Published Papers (1 paper)

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Research

18 pages, 7380 KB  
Article
Attention Mechanism-Based Micro-Terrain Recognition for High-Voltage Transmission Lines
by Ke Mo, Hualong Zheng, Zhijin Zhang, Xingliang Jiang and Ruizeng Wei
Energies 2025, 18(17), 4495; https://doi.org/10.3390/en18174495 - 24 Aug 2025
Viewed by 775
Abstract
With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and [...] Read more.
With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems. Full article
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