EFFECTS OF THE HIDDEN LAYERS IN THE HARMONIC DETECTION USING FEED FORWARD NEURAL NETWORKS

Yıl 2006, Sayı: 011, 155 - 164, 15.09.2006

Rüştü Güntürkün

Öz

In this study, the methods to apply the feed forward neural networks with two different numbers of hidden layers for harmonic detection process are described. We 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 were 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.

Anahtar Kelimeler

Artificial Neural Networks, Harmonic Detection, Hidden Layer, Active Filter

İLERİ BESLEMELİ YAPAY SİNİR AĞLARINDA KULLANILAN GİZLİ KATMAN SAYILARININ HARMONİK TANIMADA ETKİSİ

Öz

Bu çalışmada Aktif filter işlemlerinde harmonic belirleme için iki farklı gizli katman sayıları ile ileri beslemeli yapay sinir ağlı metodu tanımlanmıştır. Distorsiyonlu dalga içinden 5,7,11 ve 13. harmoniklerin simülasyonu yapılarak bu harmoniklerin yapay sinir ağının eğitimi için kullanılmıştır. Distorsiyonlu dalga 25. harmonige kadar yapay sinir ağında test için hazırlanmıştır. İleri beslemeli yapay sinir ağları harmoniklerin her birini tanımada kullanılmıştır. sonuçlar gösteriyor ki yapay sinir ağıları harmonic tanımada etkili bir şekilde kullanılabilir. İki gizli katmanlı yapay sinir ağlarının sonuçları digerlerinden daha iyidir.

Anahtar Kelimeler

Yapaya Sinir Ağları, Harmonik Belirleme, Gizli Katman, Aktif Filtre

Kaynakça

• [1] W.R.A. Ryckaert, J.A.L. Ghijselen, J.A.A. Melkebeek, Harmonic mitigation potential of shunt harmonic impedances, Electric Power Systems Research 65 (2003) 63-69
• [2] S.M.R. Rastegar, W.T. Jewell, A new approach for suppressing harmonic disturbances in distribution system based on regression analysis, Electric Power Systems Research 59 (2001) 165-184
• [3] A. Unsal, A.R. von Jouanne, V.L. Stonic, A DSP controlled resonant active filter for power conditioning in three phase industrial power system, Signal Processing, Vol. 82 (2001)1743-1752
• [4] IEEE Standarts 519-1992, IEEE Recommended Practice and Requirements for Harmonics Control in Electric Power Systems, Piscataway, NJ, (1992)
• [5] IEEE Recommended Practices for Power System Analysis, IEEE Inc., New York, NY (1992)
• [6] N. Pecharanin, M. Sone, H. Mitsui, An application of neural network for harmonic detection in active filter, ICNN (1994) 3756-3760
• [7] M. Rukonuzzaman, M. Nakaoka, Adaptive neural network based harmonic detection for active power filter, IEICE Transactions On Communications, E86B (5) (2003) 1721-1725
• [8] R. Gunturkun, N. Yumusak, F. Temurtas, Detection of Harmonics by using Artificial Neural Network, TAINN 03 Conference, July (2003)
• [9] M.M. Abdelhameed, F.F. Tolbah, A recurrent neural network based sequential controller for manufacturing automated systems, Vol. 12, (2002) 617-633
• [10] S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan Publishing Company, Englewood Cliffs, N.J. (1994)
• [11] W.E. Reid, Power quality issues – standards and guidelines, IEEE Trans. on Ind. App., Vol. 32(3) (1996) 625- 632
• [12] T.E., Nunez-Zuniga, J.A. Pomilio, Shunt active power filter synthesizing resistive loads, Vol. 17(2) (2002) 273-278