Investigation of XLPE Cable Insulation Using Electrical, Thermal and Mechanical Properties, and Aging Level Adopting Machine Learning Techniques.
LIBS
PCA
XLPE
aging
diffusion coefficient
machine learning
neural networks
trap depth
Journal
Polymers
ISSN: 2073-4360
Titre abrégé: Polymers (Basel)
Pays: Switzerland
ID NLM: 101545357
Informations de publication
Date de publication:
15 Apr 2022
15 Apr 2022
Historique:
received:
19
03
2022
revised:
05
04
2022
accepted:
13
04
2022
entrez:
23
4
2022
pubmed:
24
4
2022
medline:
24
4
2022
Statut:
epublish
Résumé
Hydrothermal and chemical aging tests on a 230 kV cross-linked polyethylene (XLPE) insulation cable were carried out in the present study to evaluate the degradation and aging levels qualitatively. The samples were subjected to water aging at a temperature of 80 °C, and in an aqueous ionic solution of CuSO
Identifiants
pubmed: 35458363
pii: polym14081614
doi: 10.3390/polym14081614
pmc: PMC9033087
pii:
doi:
Types de publication
Journal Article
Langues
eng
Références
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pubmed: 21352651
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pubmed: 30841492
Molecules. 2020 Sep 10;25(18):
pubmed: 32927806
Polymers (Basel). 2020 Dec 24;13(1):
pubmed: 33374277