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İLERİ BESLEMELİ YAPAY SİNİR AĞLARI KULLANARAK HARMONİK TANIMA

HARMONIC DETECTION USING FEED FORWARD ARTIFICIAL NEURAL NETWORKS

Journal Name:

  • Sigma Mühendislik ve Fen Bilimleri Dergisi

Publication Year:

  • 2006

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this study, the method to apply the feed forward neural networks with two different numbers of hidden layers for harmonic detection process in active filter are described. We have simulated the distorted wave including 5th, 7th, 11th, 13th harmonics and used them for training of the neural networks. The distorted wave including up to 25th harmonics were prepared for testing of the neural networks. Feed forward neural networks have been used to recognize each harmonic. The results show that these neural networks are applicable to detect each harmonic effectively. The results of the neural network with two hidden layers are better than that of the other.
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Abstract (Original Language): 
Bu çalışmada, aktif filtre işlemlerinde harmonikleri tanıma için iki farklı gizli katman ile ileri beslemeli yapay sinir ağları metodu tanımlanmıştır. Distorsiyonlu dalga içerisinden 5.,7.,11. ve 13. harmoniklerin simülasyonu yapıldı ve bunlar sinir ağlarının eğitimi için kullanıldı. Sinir ağının testi için distorsiyonlu dalga içerisinden 25. harmoniğe kadar hazırlandı. İleri beslemeli sinir ağları harmoniklerin her birini tanımak için kullanılmıştır. Sonuçlar yapay sinir ağlarının harmonikleri tanımada etkili bir şekilde kullanılabileceğini göstermektedir. Sonuc olarak iki gizli katmanlı sinir ağı diğerinden daha iyidir.
FULL TEXT (PDF): 
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  • 3
137-143
  • Turkish

REFERENCES

References: 

[1] Rastegar, S.M.R., Jewell, W.T.: A new approach for suppressing harmonic disturbances
in distribution system based on regression analysis, Electric Power Systems Research 59
(2001) 165-184.
[2] Unsal, A., von Jouanne, A.R., Stonic, V.L.: A DSP controlled resonant active filter for
power conditioning in three phase industrial power system, Signal Processing, Vol. 82
(2001) 1743-1752.
[3] IEEE Standarts 519-1992, IEEE Recommended Practice and Requirements for Harmonics
Control in Electric Power Systems, Piscataway, NJ (1992).
[4] Pecharanin, N., Sone, M., Mitsui, H.: An application of neural network for harmonic
detection in active filter, ICNN (1994), 3756-3760.
[5] Rukonuzzaman, M., Nakaoka, M: Adaptive neural network based harmonic detection for
active power filter, IEICE Transactions On Communications, E86B (5) (2003) 1721-
1725.
[6] Gunturkun, R., Yumusak, N., Temurtas, F.: Detection of Harmonics by using Artificial
Neural Network, TAINN 03 Conference, Canakkale, July (2003).
[7] Haykin, S.: Neural Networks, A Comprehensive Foundation, Macmillan Publishing
Company, Englewood Cliffs, N.J. (1994).
[8] Temurtas, F., Gunturkun, R., Yumusak, N., et.al., Harmonik detection using feed forward
and recurrent neural networks for active filters, Electric Power Systems Research
72(2004) 33-40, ELSEVIER.
[9] Reid, W.E.: Power quality issues – standards and guidelines, IEEE Trans. on Ind. App.,
Vol. 32(3) (1996) 625- 632.
[10] W.R.A. Ryckaert, J.A.L. Ghijselen, J.A.A. Melkebeek, Harmonic mitigation potential of
shunt harmonic impedances, Electric Power Syst. Res. 65 (2003) 63–69. EPSR 2019, 1–8.

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