Scopus
Harmonic detection using feed forward and recurrent neural networks for active filters
Electric Power Systems Research · Kasım 2004
Makale Bilgileri
DergiElectric Power Systems Research
Yayın TarihiKasım 2004
Cilt / Sayfa72 · 33-40
Scopus ID2-s2.0-4344605070
Özet
In this study, the methods to apply the feed forward and Elman's recurrent neural networks for harmonic detection process in active filter are described. Generally, Fourier transformation is used to analyze a distorted wave from power line, and a low pass filter is used to eliminate the fundamental wave before each harmonic component is detected. Due to this complicated process, the behaviour of active filter is delayed such that it is difficult to compensate harmonic in real time. In order to improve the processing speed and simplify harmonic detection process, the feed forward and Elman's recurrent neural networks are used to detect harmonics from distorted wave instead of Fourier transformation and low-pass filter. We simulated the distorted wave including the 5th, 7th, 11th, 13th harmonics and used these neural networks to recognize each harmonic. The results show that these neural networks are applicable to detect each harmonic effectively. © 2004 Elsevier B.V. All rights reserved.
Yazarlar (4)
1
Feyzullah Temurtas
2
R. Güntürkün
3
N. Yumusak
4
H. Temurtas
Anahtar Kelimeler
Active filters
Artificial neural network
Harmonic compensation
Harmonic detection
Kurumlar
Dumlupinar Üniversitesi
Kutahya Turkey
Sakarya Üniversitesi
Serdivan Turkey
Metrikler
44
Atıf
4
Yazar
4
Anahtar Kelime