Power system transformerboard degradation detection using probabilistic neural network
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Asian Research Publishing Network (ARPN)
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The insulation condition monitoring of a power transformer has an important role for insulating materials which are subjected to extensive breakdown stress. In this study, a test setup has been constructed in order to simulate real world breakdown characteristics of transformerboards which are widely used as the insulating material. During the service life transformerboards may display undesired surface discharge damage due to increased rated voltages, which reduces the lifetime of transformerboards. The probabilistic neural network is used to detect the surface degradation of a transformerboard by analyzing electrical and ultrasound discharge data obtained from the test setup. The principle component analysis is employed to eliminate the messy matrix and vector calculations of the probabilistic neural network operations. Results of the classification procedure are given. © 2005 - 2012 JATIT & LLS.
Açıklama
Anahtar Kelimeler
Principle component analysis, Probabilistic neural network, Transformerboard
Kaynak
Journal of Theoretical and Applied Information Technology
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
42
Sayı
1
