⚙
AI-generated deep summary
by claude@2026-07, 2026-07-06
· read from full text
ⓘ
The paper studies partial discharge behavior in the insulation of a dry-type sub-transmission transformer under standard lightning impulse (SLI) voltages, aiming to determine partial discharge inception voltage (PDIV) and the corresponding inception electric field intensity (PDIE). Using a hybrid experimental–FEM framework, the authors built an experimental transformer model (two disk-type windings, a grounded core model, and inter-disk insulating spacers) to measure PDIV and approximate inception time via a High-Frequency Current Transformer (HFCT), then used those PDIV values in COMSOL Multiphysics to compute electric field distributions and derive PDIE. The key finding is that the combined experimental FEM approach can accurately estimate PD inception parameters under SLI voltage, with the study framing this as relevant to increased insulation failure probability due to SLI-induced partial discharges in existing winding insulation defects. The paper’s major caveat is that its method depends on the constructed model setup and on the approximation of inception time used for subsequent FEM-based electric field determination. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Full text
6,851 characters
· extracted from
preprint-html
· click to expand
An Experimental-FEM Method for Predicting PDIV and PDIE of a Dry-Type Sub-Transmission Transformer under SLI Voltage | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 February 2026 V1 Latest version Share on An Experimental-FEM Method for Predicting PDIV and PDIE of a Dry-Type Sub-Transmission Transformer under SLI Voltage Authors : Mohamadreza Namavar Zeyni Vandi 0009-0005-0961-5516 [email protected] and Mehdi Vakilian 0000-0003-2925-5077 Authors Info & Affiliations https://doi.org/10.22541/au.177190217.72773530/v1 120 views 59 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Ensuring immunity of insulation in dry-type sub-transmission transformers against PD effects necessitates maintaining the electric field intensity below PD inception electric field intensity (PDIE) under both steady-state and transient voltages. In this paper PD inception voltage (PDIV) and corresponding PDIE under standard lightning impulse (SLI) voltage are determined. These parameters are fundamental to transformer insulation design. This paper proposes a hybrid analytical framework which combines the measurements on an experimental setup with finite element method (FEM) modeling to determine the PD inception parameters under SLI voltage. Experimental setup includes: two disk-type windings built according to their design, a grounded core model, and inter-disk insulating spacers to replicate actual operating conditions of a dry-type transformer. This model accurately estimates the PD inception parameters. The PDIV and approximate PD inception time under SLI voltage are determined experimentally utilizing a High-Frequency Current Transformer (HFCT). Subsequently, applying the SLI PDIV to the transformer winding model in COMSOL Multiphysics software yields electric field distribution which enables determination of the corresponding PDIE. This study is concerned with an increase in the probability of transformer insulation failure due to the development of SLI-induced PDs in the existing defects of the winding insulation in a dry-type transformer. Supplementary Material File (iet-hve-template-doc.docx) Download 1.44 MB Information & Authors Information Version history V1 Version 1 24 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords air gaps dielectric materials electric fields finite element analysis lightning partial discharge measurement partial discharges power transformer insulation transients Authors Affiliations Mohamadreza Namavar Zeyni Vandi 0009-0005-0961-5516 [email protected] Sharif University of Technology Electrical Engineering Department View all articles by this author Mehdi Vakilian 0000-0003-2925-5077 Sharif University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 120 views 59 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohamadreza Namavar Zeyni Vandi, Mehdi Vakilian. An Experimental-FEM Method for Predicting PDIV and PDIE of a Dry-Type Sub-Transmission Transformer under SLI Voltage. Authorea . 24 February 2026. DOI: https://doi.org/10.22541/au.177190217.72773530/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177190217.72773530/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe34c1ca9e5ad07',t:'MTc3OTE5NTc2MA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.