• Türkçe
    • English
  • Türkçe 
    • Türkçe
    • English
  • Giriş
Öğe Göster 
  •   DSpace Ana Sayfası
  • 6.Araştırma Çıktıları / Research Outcomes(WOS-Scopus-TR-Dizin-PubMed)
  • Scopus İndeksli Yayınlar Koleksiyonu
  • Öğe Göster
  •   DSpace Ana Sayfası
  • 6.Araştırma Çıktıları / Research Outcomes(WOS-Scopus-TR-Dizin-PubMed)
  • Scopus İndeksli Yayınlar Koleksiyonu
  • Öğe Göster
JavaScript is disabled for your browser. Some features of this site may not work without it.

Online power quality events detection using weighted Extreme Learning Machine

Thumbnail
Tarih
2018
Yazar
Ucar, F. and Alcin, O.F. and Dandil, B. and Ata, F. and Cordova, J. and Arghandeh, R.
Üst veri
Tüm öğe kaydını göster
Özet
This paper proposes a novel method for online power quality events classification using a machine learning based Weighted Extreme Learning Machine (W-ELM) classifier. For its fast response, easy to build structure and superior generalization, ELM is a preferred algorithm for different fields. W-ELM is an enhanced version of basic ELM whose striking feature is using a weight function for more effective classifying. Permutation entropy, local peaks, and LombScargle periodogram compose the powerful feature set with their powered ability to reveal the distinctiveness and low computational cost comparing to transform based methods. Dataset consisting of real site actual signals has been gathered from Turkish electricity transmission system. Most occurred events like voltage sag, swell, interruption and harmonics are included. We also assign conventional methods as artificial neural network and support vector machine with a basic ELM for a though analyze. Results prove that proposed system has the ability to operate both normal and faulty operations with its fast computational effort capable of online systems. Analyze studies on real-world PQ signals prove the validity of the presented algorithm. © 2018 IEEE.
Bağlantı
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050689200&doi=10.1109%2fSGCF.2018.8408938&partnerID=40&md5=679d3770fec0f4596e641af6f93f2b0b
http://acikerisim.bingol.edu.tr/handle/20.500.12898/4311
Koleksiyonlar
  • Scopus İndeksli Yayınlar Koleksiyonu [1357]





Creative Commons License
DSpace@BİNGÖL by Bingöl University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace software copyright © 2002-2016  DuraSpace
İletişim | Geri Bildirim
Theme by 
Atmire NV
 

 



| Politika | Rehber | İletişim |

sherpa/romeo

Göz at

Tüm DSpaceBölümler & KoleksiyonlarTarihe GöreYazara GöreBaşlığa GöreKonuya GöreBy TypeBu KoleksiyonTarihe GöreYazara GöreBaşlığa GöreKonuya GöreBy Type

Hesabım

GirişKayıt

DSpace software copyright © 2002-2016  DuraSpace
İletişim | Geri Bildirim
Theme by 
Atmire NV