TY - JOUR T1 - Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması TT - Classification of Power Quality Disturbances with 2D Discrete Wavelet Transform and Bagged Decision Trees Method AU - Saraç, Zehra AU - Karasu, Seçkin PY - 2018 DA - December DO - 10.2339/politeknik.391795 JF - Politeknik Dergisi PB - Gazi University WT - DergiPark SN - 2147-9429 SP - 849 EP - 855 VL - 21 IS - 4 LA - tr AB - Bu çalışmada, Güç Kalitesi (GK)bozulmalarının sınıflandırılması için 2 Boyutlu Ayrık Dalgacık Dönüşümü(2B-ADD) yöntemi ile öznitelikler çıkartılmakta ve Destek Vektör Makineleri(DVM), Yapay Sinir Ağları (YSA) ve Torbalama Karar Ağaçları (TKA) yöntemleriile sınıflandırma işlemi yapılmaktadır. Gürültülü (40 dB, 30 dB ve 20 dB) vegürültüsüz durumları içeren 11 farklı GK bozulması için toplamda 2200 adetsinyal sentetik olarak üretilmektedir. Sinyaller 2 boyutlu görüntü matrislerineçevrilmekte ve her birine 2B-ADD uygulanmaktadır. Farklı ayrıştırma seviyesi veistatistiksel özellikler uygulanarak öznitelikler oluşturulmaktadır.Özniteliklerden en uygun olanları Sıralı İleri Seçim (SİS) ve ReliefFyöntemleri ile seçilmektedir. Benzetim çalışmasına göre 3 farklısınıflandırıcının başarımı birbirleri ile kıyaslanmaktadır. Sıralı ileri seçimile seçilen öznitelikleri kullanan TKA yönteminin %99.12±0.12 oranı ile en iyibaşarımı veren yöntem olduğu görülmektedir. KW - Güç kalitesi KW - 2 boyutlu ayrık dalgacık dönüşümü KW - torbalama karar ağaçları sınıflandırıcısı KW - sıralı ileri seçim KW - ReliefF N2 - In this study, to classify Power Quality (PQ)disturbances, attributes are extracted by 2D Discrete Wavelet Transform(2D-DWT) method and Support Vector Machines, Artificial Neural Networks andBagged Decision Trees (BDT) methods are used forclassification stage. 2200signals are synthetically producedfor 11 different PQ disturbances, including noisy (40 dB, 30 dB and 20dB) and noiseless states. Signals are transformed into 2D image matrices and 2DDWT is applied to each. Attributes are created by applying different level ofdecomposition and statistical properties. The most appropriate ones areselected with Sequential Forward Selection (SFS) and ReliefF methods. BDTmethod, which uses selected attributes with SFS, is the method that gives thebest performance with a rate of 99.12±0.12%. CR - [1] Thirumala K., Jain T. and Umarikar A.C., "Visualizing time-varying power quality indices using generalized empirical wavelet transform", Electric Power Systems Research, 143: 99-109, (2017). CR - [2] Nashad N.R., Islam M.J., Alam S., Rahat R.M., Begum M.T. and Alam M.R, "A simplistic mathematical approach for detection and classification of power quality events", Electrical, Computer and Communication Engineering (ECCE), International Conference on IEEE, 698-703, (2017). 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