OG Kablo Başlıklarında Rezidüel Sinir Ağları Temelli Kusur Sınıflandırılması

Halil İbrahim Üçkol, Suat İlhan

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

This paper presents a method based on residual neural networks to classify poor workmanship defects located on MV cable terminations. A total of two cable groups was used, and each group consists of five different cables with five defects. Each cable has one defect. 120 partial discharge patterns were acquired for each defect (1200 patterns in total). One group of cables was used in the training phase and the other in the testing phase. Residual neural networks, one of the deep learning algorithms, were used in the analysis and classification of the data. The results show that the residual neural network algorithm can be used as an efficient diagnostic tool for classifying poor workmanship defects in cable termination, and partial discharge measurements provide valuable information in classifying defects.

Tercüme edilen katkı başlığıDefect classification in MV cable terminations based on residual neural network
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2020 12th International Conference on Electrical and Electronics Engineering, ELECO 2020
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar6-9
Sayfa sayısı4
ISBN (Elektronik)9786050113310
DOI'lar
Yayın durumuYayınlandı - 26 Kas 2020
Etkinlik12th International Conference on Electrical and Electronics Engineering, ELECO 2020 - Bursa, Turkey
Süre: 26 Kas 202028 Kas 2020

Yayın serisi

Adı2020 12th International Conference on Electrical and Electronics Engineering, ELECO 2020

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???event.eventtypes.event.conference???12th International Conference on Electrical and Electronics Engineering, ELECO 2020
Ülke/BölgeTurkey
ŞehirBursa
Periyot26/11/2028/11/20

Bibliyografik not

Publisher Copyright:
© 2020 Turkish Chambers of Electrical Engineers.

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