Enerji iletim Hatlarında oluşan arızaların aşırı öğrenme makinesi ile tespiti
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Dosyalar
Tarih
2013-06-13
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
With the increase of energy demand continuous energy transmission gained considerable attention. For a continuous energy transmission, the faulty power transmission line needs to be quickly isolated from the system. In this study, Extreme Learning Machine (ELM) possessing fast learning and high generalization capacity was used for this purpose and it was found as showing a good performance in detecting the faulty transmission line. In the study real fault signals recorded from transmission lines were used. A feature vector was formed from a cycle of the energy signal using relative entropy and classified via ELM. The obtained results were compared with the ones obtained through SVM, YSA, NB, J48 and PART learning techniques and the ones obtained in the previous studies. According the obtained results ELM both in terms of speed and performance was found superior.
Açıklama
Anahtar Kelimeler
Enerji İletim Hattı, Arıza Tespiti, Bağıl Entopi, Aşırı Makine Öğrenmesi, Component, ELM, Fault Detection, Relative Entropy, Transmission Line
Kaynak
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N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Ertuğrul, Ö. F., Tağluk, M. E., Kaya, Y. (2013).Enerji iletim Hatlarında oluşan arızaların aşırı öğrenme makinesi ile tespiti. 2013 21st Signal Processing and Communications Applications Conference (SIU), 24-26 April 2013, Haspolat, Turkey. https://doi.org/10.1109/siu.2013.6531209
