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

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Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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.

Açıklama

Anahtar Kelimeler

Mühendislik, Elektrik ve Elektronik, Bilgisayar Bilimleri, Yazılım Mühendisliği, Bilgisayar Bilimleri, Sibernitik, Bilgisayar Bilimleri, Bilgi Sistemleri, Bilgisayar Bilimleri, Donanım ve Mimari, Bilgisayar Bilimleri, Teori ve Metotlar, Bilgisayar Bilimleri, Yapay Zeka

Kaynak

Zeki sistemler teori ve uygulamaları dergisi (Online)

WoS Q Değeri

Scopus Q Değeri

Cilt

5

Sayı

1

Künye