Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging-Reference-Cited by-同舟云学术

Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging

Published:2016-03-15 Issue:11 Volume:28 Page:3557-3570
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Boukezzi L.,Bessissa L.,Boubakeur A.,Mahi D.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2259-y/fulltext.html

Reference44 articles.

1. Ilie S, Setnescu R, Lungulescu EM, Marinescu V, Ilie D, Setnescu T, Mares G (2011) Investigations of a mechanically failed cable insulation used in indoor conditions. Polym Test 30:173–182

2. http://www.nationalgrid.com/NR/rdonlyres/C9AF5612-DF25-4FCF-ABB5-69F237ACB0C5/3411/SPTTS25.pdf . Accessed 11 May 2014

3. Yamanaka T, Maruyama S, Tanaka T (2003) The development of DC ± 500 kV XLPE cable in consideration of the space charge accumulation. In: IEEE 7th international conference on properties and applications of dielectric materials (ICPADM), Nagoya, Japan, pp 689–694

4. Dissado LA, Fothergill JH (1992) Electrical degradation and breakdown in polymers. Peter Peregrinus Ltd, London

5. Plopeanu M, Notingher PV, Stancu C, Grigorescu S (2011) Electrical ageing of polyethylene power cables insulation subjected to an electric field in the presence of water. In: 7th international symposium on advanced topics in electrical engineering, Bucharest, pp 1–4

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