Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

Year 2018, , 849 - 855, 01.12.2018

Seçkin Karasu Zehra Saraç

https://doi.org/10.2339/politeknik.391795

Abstract

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) yöntemleri
ile sınıflandırma işlemi yapılmaktadır. Gürültülü (40 dB, 30 dB ve 20 dB) ve
gürültüsüz durumları içeren 11 farklı GK bozulması için toplamda 2200 adet
sinyal sentetik olarak üretilmektedir. Sinyaller 2 boyutlu görüntü matrislerine
çevrilmekte ve her birine 2B-ADD uygulanmaktadır. Farklı ayrıştırma seviyesi ve
istatistiksel özellikler uygulanarak öznitelikler oluşturulmaktadır.
Özniteliklerden en uygun olanları Sıralı İleri Seçim (SİS) ve ReliefF
yöntemleri ile seçilmektedir. Benzetim çalışmasına göre 3 farklı
sınıflandırıcının başarımı birbirleri ile kıyaslanmaktadır. Sıralı ileri seçim
ile seçilen öznitelikleri kullanan TKA yönteminin %99.12±0.12 oranı ile en iyi
başarımı veren yöntem olduğu görülmektedir.

Keywords

Güç kalitesi, 2 boyutlu ayrık dalgacık dönüşümü, torbalama karar ağaçları sınıflandırıcısı, sıralı ileri seçim, ReliefF

Classification of Power Quality Disturbances with 2D Discrete Wavelet Transform and Bagged Decision Trees Method

Abstract

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 and
Bagged Decision Trees (BDT) methods are used for
classification stage. 2200
signals are synthetically produced
for 11 different PQ disturbances, including noisy (40 dB, 30 dB and 20
dB) and noiseless states. Signals are transformed into 2D image matrices and 2D
DWT is applied to each. Attributes are created by applying different level of
decomposition and statistical properties. The most appropriate ones are
selected with Sequential Forward Selection (SFS) and ReliefF methods. BDT
method, which uses selected attributes with SFS, is the method that gives the
best performance with a rate of 99.12±0.12%.

Keywords

Power quality, 2D discrete wavelet transform, bagged trees classifier, sequential forward selection, ReliefF

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