Classification of power quality events signals with pattern recognition methods by using Hilbert transform and genetic algorithms [Güç kalitesi bozulma sinyallerinin hilbert dönüşümü ve genetik algoritmalar kullanilarak örüntü tanima yöntemleri ile siniflandirilmasi]

In this study, instantaneous envelope, phase and frequency series are obtained by Hilbert transform for Power Quality (PQ-Power Quality) disturbances signals. Rms, Thd, energy, entropy and statistical properties are applied to these series. With the wrapper feature selection approach, a set of features is obtained that has a small number of feature subset and a high performance from 36 features. Genetic Algorithm (GA) is used as a search algorithm and the classifier algorithm is K nearest neighborhood (KNN). Support Vector Machines (SVM) for selected features are also used in the classification step. The learning algorithm is obtained as KNN, the model performance that classifies PQ classes with 99.07%. The number of feature sets is 8. In addition, performance under noisy data is also tested to show that the generated model has a generalized structure. © 2018 IEEE.

Yazar Karasu S.
Sarac Z.
Yayın Türü Conference Object
Tek Biçim Adres https://hdl.handle.net/20.500.12628/4690
Tek Biçim Adres 10.1109/SIU.2018.8404669
Konu Başlıkları Classification
Genetic algorithm
Hilbert transform
Pattern recognition
Power quality
Koleksiyonlar Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD
Scopus İndeksli Yayınlar Koleksiyonu
Dergi Adı 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Sayfalar 1 - 4
Yayın Yılı 2018
Eser Adı
[dc.title]
Classification of power quality events signals with pattern recognition methods by using Hilbert transform and genetic algorithms [Güç kalitesi bozulma sinyallerinin hilbert dönüşümü ve genetik algoritmalar kullanilarak örüntü tanima yöntemleri ile siniflandirilmasi]
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
conferenceObject
Açıklama
[dc.description]
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas
Açıklama
[dc.description]
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
Özet
[dc.description.abstract]
In this study, instantaneous envelope, phase and frequency series are obtained by Hilbert transform for Power Quality (PQ-Power Quality) disturbances signals. Rms, Thd, energy, entropy and statistical properties are applied to these series. With the wrapper feature selection approach, a set of features is obtained that has a small number of feature subset and a high performance from 36 features. Genetic Algorithm (GA) is used as a search algorithm and the classifier algorithm is K nearest neighborhood (KNN). Support Vector Machines (SVM) for selected features are also used in the classification step. The learning algorithm is obtained as KNN, the model performance that classifies PQ classes with 99.07%. The number of feature sets is 8. In addition, performance under noisy data is also tested to show that the generated model has a generalized structure. © 2018 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Yılı
[dc.date.issued]
2018
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/SIU.2018.8404669
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4690
Yayın Dili
[dc.language.iso]
tur
Konu Başlıkları
[dc.subject]
Classification
Konu Başlıkları
[dc.subject]
Genetic algorithm
Konu Başlıkları
[dc.subject]
Hilbert transform
Konu Başlıkları
[dc.subject]
Pattern recognition
Konu Başlıkları
[dc.subject]
Power quality
Yazar
[dc.contributor.author]
Karasu S.
Yazar
[dc.contributor.author]
Sarac Z.
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Yazar Departmanı
[dc.contributor.department]
Zonguldak Bülent Ecevit Üniversitesi
İlk Sayfa Sayısı
[dc.identifier.startpage]
1
Son Sayfa Sayısı
[dc.identifier.endpage]
4
Dergi Adı
[dc.relation.journal]
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
ISBN
[dc.identifier.isbn]
9781538615010
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
18
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
06 Şubat 2024 23:05
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Tıklayınız
obtained feature features performance algorithm number series classification classifies learning instantaneous classes envelope selected Machines Vector Support neighborhood tested structure generalized generated addition nearest (PQ-Power approach selection Quality) wrapper disturbances Quality signals applied properties statistical
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