Stockwell Dönüşümü Tabanlı Güç Kalitesi Bozunumlarının Destek Vektör Makinası ve Yapay Sinir Ağları ile Sınıflandırılması

Elektrik enerjisi hizmetlerinin kesintisiz bir biçimde tüketiciye ulaştırılması büyük önem taşımaktadır. Sistemdeki bozulmaların tespiti ve alınması gereken önlemler bu açıdan önemlidir. Elektrik sinyalindekini bozulmaların türünün belirlenmesi için çeşitli özellik çıkarım yöntemleri kullanılmaktadır. Bu çalışmada, elektrik güç sistemlerinde meydana gelen Güç Kalitesi Bozunumlarından(GKB) gerilim yükselmesi, gerilim çökmesi, harmonikli gerilim, harmonikli gerilim düşmesi, harmonikli gerilim yükselmesi, flicker ve transient ile referans sinyali olarak saf sinüs sinyallerini içeren sekiz işaret toplam on dönem sürecek şekilde TS EN 50160 standartlarına göre MATLAB ortamında oluşturulmuştur. Belirlenen GKB’na ait özellik çıkarımı için kullanılan yöntemlerden biri olan Stockwell-Dönüşümü ile frekans-genlik, zaman-genlik, geometrik ortalama ve standart sapma olmak üzere 4 çeşit özellik çıkarımı yapılmıştır. Bu özellikler üzerinden gerilim bozulmalarının tespiti yorumlanmıştır. Toplam 640 benzetim verisi Destek Vektör Makinaları (DVM) ve Yapay Sinir Ağları(YSA) ile sınıflandırıcıya sokularak sınıflandırma başarımları karşılaştırılmıştır.

Anahtar Kelimeler:

Güç Kalitesi Bozunumları, Stockwell dönüşümü, Destek Vektör Makinaları, Yapay Sinir Ağları.

Classification of Stockwell Transform Based Power Quality Disturbance with Support Vector Machine and Artificial Neural Networks

The detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is very important to generate electrical energy and to deliver this energy to the end-user equipment at an acceptable voltage. Various property extraction methods are used to determine the type of disturbances in the electrical signal. In this study, seven power distortions including voltage sag, voltage swell, voltage harmonics, voltage sag with harmonics, voltage swell with harmonics, flicker, transient signals and pure sine as a reference signal is used. Synthetic data are produced in MATLAB using parametric equations based on TS EN 50160 standard. Four kinds of feature extraction as frequency-amplitude, time-amplitude, geometric mean and standard deviation is made with Stockwell Transform (ST), which is one of the methods used for the feature extraction of the determined GKB. Detection of voltage distortions is interpreted through these properties. 640 simulation data is entered into the classifier by using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and their classification performance is compared.

Keywords:

Power Quality Disturbance, Stockwell Transform, Support Vector Machine, Artificial Neural Network.,

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