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Novel Advances in High Voltage Insulation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1465

Special Issue Editors


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Guest Editor
School of Engineering, Cardiff University, Cardiff CF10 3AT, UK
Interests: high voltage insulation

E-Mail Website
Guest Editor
School of Engineering, Cardiff University, Cardiff CF10 3AT, UK
Interests: insulation coordination at altitude; atmospheric correction in electrical insulation systems; lightning direct effects; lightning electrostatics and electromagnetics; earthing and grounding; ecological impacts of lightning; high voltage and high current measurement techniques; optical sensing and measurement for harsh electromagnetic environments; computational electromagentics

Special Issue Information

Dear Colleagues,

This Special Issue highlights the latest developments in high-voltage insulation, emphasizing the incorporation and influence of machine learning and exploring additional aspects of this discipline. Its goal is to combine machine learning insights with advancements in high-voltage insulation materials, their design, condition monitoring, and diagnostics in a multidisciplinary manner.

We welcome submissions that investigate the use of machine learning methods in forecasting and detecting insulation issues, improving the dependability of high-voltage systems, and optimizing maintenance approaches. Submissions should showcase the successful integration of machine learning algorithms with high-voltage insulation technologies, addressing the obstacles and advancements in the field.

This Special Issue also invites research on innovative insulation materials and their characteristics, sophisticated production methods for insulation systems, and creative approaches for testing and evaluating insulation. Research on the impact of the environment and ageing on high-voltage insulation, along with the creation of environmentally friendly and long-lasting insulating materials, is also a focus of interest.

This extensive compilation seeks to encompass theoretical research and practical case studies to offer a complete perspective on current trends and potential advancements in high-voltage insulation technology. This platform facilitates the sharing of findings and ideas among researchers, engineers, and industry professionals in the field of high-voltage insulation. It aims to drive innovative solutions using machine learning and advancements in material science and engineering.

Dr. Rahmat Ullah
Dr. David Clark
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

  • condition monitoring
  • modern insulation system
  • insulation aging
  • life expectancy
  • machine learning in insulation system

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Published Papers (1 paper)

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Research

16 pages, 4817 KiB  
Article
Degradation Assessment of In-Service Transformer Oil Based on Electrical and Chemical Properties
by Ehtasham Mustafa, Bilal Ahmad, Malik Imran Ali, Ramy S. A. Afia and Rahmat Ullah
Appl. Sci. 2024, 14(24), 11767; https://doi.org/10.3390/app142411767 - 17 Dec 2024
Viewed by 1003
Abstract
In order to ensure the long-term reliability and safety of power transformers, it is important to continuously monitor the characteristics of insulating oil, which not only helps in understanding its behavior over time but also ensures the safety of the equipment. The current [...] Read more.
In order to ensure the long-term reliability and safety of power transformers, it is important to continuously monitor the characteristics of insulating oil, which not only helps in understanding its behavior over time but also ensures the safety of the equipment. The current study analyzes in-service insulating oil with the aim of relating deterioration and changes in the oil with service aging. Insulating oil samples were collected from three power transformers, with a voltage level of 220 kV and 132 kV, installed at a 220 kV substation. Electrical and chemical characteristics were obtained, and the impact of service aging and the relationships among load variation, oil, and winding temperatures with the characteristics were evaluated. Variations in the dielectric dissipation factor and breakdown voltage with service aging were recorded for all transformers, while the moisture content increased with each service year. Among the concentrations of gases present in the insulating oil, carbon monoxide, oxygen, and nitrogen concentrations increased after each service year. The impact of load variation on the breakdown voltage of the 132 kV transformer oil was more prominent than for the 220 kV transformers. The analysis of gas ratios and moisture content identified the degradation of cellulose insulation in all transformers, which was due to the presence of electrical faults. Full article
(This article belongs to the Special Issue Novel Advances in High Voltage Insulation)
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