İleri beslemeli ve elman geri beslemeli yapay sinir ağlarını kullanarak harmoniklerin kompanzasyonu
Harmonics compensation using feed forward and elman recurrent artificial neural networks
- Tez No: 136450
- Danışmanlar: YRD. DOÇ. DR. NEJAT YUMUŞAK
- Tez Türü: Doktora
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: îleri beslemeli sinir ağı, Geri beslemeli sinir ağı, Hızlı geriye yayılım, Standart geriye yayılma, Harmonik bozulması, Aktif filtre, Güç kalitesi
- Yıl: 2003
- Dil: Türkçe
- Üniversite: Sakarya Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Elektrik-Elektronik Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Elektronik Bilim Dalı
- Sayfa Sayısı: 181
Özet
ÖZET
Özet (Çeviri)
HARMONICS COMPENSATION USING FEED FORWARD AND ELMAN RECURRENT ARTIFICIAL NEURAL NETWORKS SUMMARY Keywords : Feed forward neural network, Elman's recurrent neural network, Fast and Standart backpropagation, Harmonics distortion, Active filter, Power quality In this study, the methods to apply the feed forward with two hidden layers and Elman's recurrent neural network for harmonic detection process in active filter are proposed. Two type neural networks with one and two hidden layers are used for this purpose. At the first step, we used three layers networks (input layer, hidden layer and output layer) with standart backpropagation and fast backpropagation learning algoritm. The hidden layer neurons and the output layer neurons use nonlinear sigmoid activation functions. In alternative networks, the output layer neurons use linear activation functions for comparison. At the second step, we used four layers networks (input layer, two hidden layers and output layer) with fast backpropagation learning algoritm. The hidden layer neurons and the output layer neurons use nonlinear sigmoid activation functions. For the training and test processes, input signals of the neural networks are the amplitudes of one period distorted wave. The amplitudes are taken 128 point at regular interval of time axis. The amplitudes are used to be input signals of the neural networks without any pre-processing. In order to make neural network enable to detect harmonics from distorted wave, it is necessary to use some representative distorted waves for learning. These distorted waves are made by mixing the component of the 5th, 7th, 11th, and 13 h harmonics in fundamental wave. For this purpose, 5th harmonic up to 70%, 7th harmonic up to 40%, 11th harmonic up to 10% and 13th harmonic up to 5% were used and approximately 2500 representative distorted waves were generated for training process. During the training process, the distorted waves were used for recognition. For the performance evaluation of the neural network structures, 5th harmonic up to 70%, 7th harmonic up to 40%, 11th harmonic up to 10% and 13th harmonic up to 5%, 17th harmonic up to 5%, 19th harmonic up to 2.5%, 23th harmonic up to 2.5%, 25th harmonic up to 2% were used and approximately 250 representative distorted waves were generated as a test set. After the training process is completed, the general distorted waves (test set) were used for recognition. As the result of recognitions at the training and test phase, output signal from each output unit means the content of each harmonic including the input distorted wave and these harmonics are eliminated from the distorted wave. As the result, the possibility of the feed forward and Elman's recurrent neural networks to detect harmonics is confirmed by compensating the distorted waves and it can be said that the feed forward and Elman's recurrent neural networks are effectively to be used for active filter. xxiv
Benzer Tezler
- Approximate analysis and condition assesment of reinforced concrete T-beam bridges using artificial neural networks
Betonarme T-kiriş köprülerin yapay sinir ağlarını kullanarak yaklaşık analizi ve durum tespiti
TAHA DUMLUPINAR
Yüksek Lisans
İngilizce
2008
İnşaat MühendisliğiOrta Doğu Teknik Üniversitesiİnşaat Mühendisliği Ana Bilim Dalı
YRD. DOÇ. DR. OĞUZHAN HASANÇEBİ
- Geri beslemeli yapay sinir ağlarının genetik operatörlere dayalı tabu araştırma algoritması kullanarak eğitilmesi
Training recurrent neural networks using tabu search based on genetic operators
ADEM KALINLI
- Performans artırmaya yönelik paralel mimarilerin yapay sinir ağları yaklaşımı ile değerlendirilmesi
Performance designed architectures: A neural network approach
SIRMA YAVUZ
Doktora
Türkçe
2006
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolYıldız Teknik ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
PROF.DR. OYA KALIPSIZ
- Yapay sinir ağları ile karakter algılama
Pattern recognition with neural networks
HACI BAYRAM KILIÇ
Yüksek Lisans
Türkçe
1998
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolGazi ÜniversitesiElektronik ve Bilgisayar Eğitimi Ana Bilim Dalı
DOÇ. DR. ÇETİN ELMAS
- Karma iletim hatlarında mesafe koruma rölesi çalışmasının incelenmesi ve çalışma başarımlarının yükseltilmesi
Investigation of distance protection relay operation in mixed transmission lines and improving operation performans
SERKAN BUDAK
Yüksek Lisans
Türkçe
2020
Elektrik ve Elektronik MühendisliğiKonya Teknik ÜniversitesiElektrik-Elektronik Mühendisliği Ana Bilim Dalı
DOÇ. DR. BAHADIR AKBAL