TBMF Framework: A Transformer-Based Multilevel Filtering Framework for PD Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F23%3A10252664" target="_blank" >RIV/61989100:27730/23:10252664 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/23:10252664
Result on the web
<a href="https://ieeexplore.ieee.org/document/10124857/metrics#metrics" target="_blank" >https://ieeexplore.ieee.org/document/10124857/metrics#metrics</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TIE.2023.3274881" target="_blank" >10.1109/TIE.2023.3274881</a>
Alternative languages
Result language
angličtina
Original language name
TBMF Framework: A Transformer-Based Multilevel Filtering Framework for PD Detection
Original language description
Partial discharge (PD) of overhead lines is an indication of imminent dielectric breakdown and a cause of insulation degradation. Efficient PD detection is the significant foundation of electrical system maintenance. This paper proposes a transformer-based multilevel filtering (TBMF) framework for PD detection. It creates the multilevel filtering mechanism to be robust to large-scale industrial measurements contaminated with a variety of background noises and plenty of invalid information. The primary filtering innovatively creates the principle of possible PD measurements to replace feature extraction and reduce manual intervention. For the first time, multiple transformer-based algorithms are introduced to the PD detection field to process the possible PD measurements without relying on the sequence order. The secondary filtering then refines the segmentation-level results from the primary filtering and outputs the overall detection results. Multiple numerical algorithms, AI models, and intelligent meta-heuristic optimization have been adopted as methodologies of the secondary filtering. The TBMF framework is experimentally verified by extensive field trial data of medium voltage overhead power lines. Its detection accuracy reaches 96.1<inline-formula><tex-math notation="LaTeX">$%$</tex-math></inline-formula>, which outperforms other techniques in the literature. It provides an economic and complete PD detection solution to maintain the economical and safe operation of power systems. IEEE
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Industrial Electronics
ISSN
0278-0046
e-ISSN
1557-9948
Volume of the periodical
4
Issue of the periodical within the volume
71
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
1-10
UT code for WoS article
001103021200080
EID of the result in the Scopus database
2-s2.0-85162858417