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

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dc.contributor.authorGüney, Ezgi
dc.contributor.authorÇakmak, Ozan
dc.contributor.authorKocaman, Cagri
dc.date.accessioned2025-03-23T19:09:01Z
dc.date.available2025-03-23T19:09:01Z
dc.date.issued2022
dc.departmentSinop Üniversitesi
dc.description.abstractThe 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.
dc.identifier.doi10.38016/jista.996541
dc.identifier.endpage84
dc.identifier.issn2651-3927
dc.identifier.issue1
dc.identifier.startpage75
dc.identifier.trdizinid507886
dc.identifier.urihttps://doi.org/10.38016/jista.996541
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/507886
dc.identifier.urihttps://hdl.handle.net/11486/3247
dc.identifier.volume5
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofZeki sistemler teori ve uygulamaları dergisi (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250323
dc.subjectMühendislik
dc.subjectElektrik ve Elektronik
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectBilgisayar Bilimleri
dc.subjectSibernitik
dc.subjectBilgisayar Bilimleri
dc.subjectBilgi Sistemleri
dc.subjectBilgisayar Bilimleri
dc.subjectDonanım ve Mimari
dc.subjectBilgisayar Bilimleri
dc.subjectTeori ve Metotlar
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.titleClassification of Stockwell Transform Based Power Quality Disturbance with Support Vector Machine and Artificial Neural Networks
dc.typeArticle

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