Classification of Power Quality Disturbances with Hilbert-Huang Transform, Genetic Algorithm and Artificial Intelligence/Machine Learning Methods

Year 2020, Volume: 23 Issue: 4, 1219 - 1229, 01.12.2020

Seçkin Karasu , Zehra Saraç

https://doi.org/10.2339/politeknik.508773

Cited By: 7

Abstract

In this study, Hilbert-Huang Transform method and
statistical features are obtained to classify Power Quality (PQ) Disturbances.
The appropriate features are selected by the Genetic Algorithm (GA) and
k-Nearest Neighbor classification approach. Models based on Artificial
Intelligence and Machine Learning methods are formed and test process is
performed by using data from experimental setup. It is produced by using
mathematical equations together with noisy conditions (40 dB, 30 dB and 20 dB).
In addition, Power Quality Disturbances data from the experimental setup is
also used in this study. First of all, Empirical Mode Decomposition (EMD)
method is applied to the signals. Then, by applying Hilbert transformation
(HT), statistical features and necessary features are extracted. The same
procedure is repeated for Ensemble Empirical Mode Decomposition (EEMD). GA +
KNN wrapper approach is used to select the required ones according to the
number of extracted features. Power Quality Disturbances models are created
based on Multilayer Perceptron (MLP) and k-Nearest Neighbour classifier (KNN)
methods. The performance of EEMD + HT + GA + KNN classification model for 9
single and 9 multiple types of disruption is 99.15% for synthetic data and
99.02% for experimental data.  Compared
to the literature, EEMD + HT + GA + KNN method has the ability to distinguish 9
multiple PQ disturbances and the overall performance gives the best performance
with a rate of 99.12%.

Keywords

Power quality disturbances, Hilbert-Huang transform, emprical mode decompositon mod, ensemble emprical mode decomposition, genetik algorithm

Güç Kalitesi Bozulmalarının Hilbert-Huang Dönüşümü, Genetik Algoritma Ve Yapay Zeka/Makine Öğrenmesi Yöntemleri İle Sınıflandırılması

Abstract

Bu
çalışmada Güç Kalitesi (GK) Bozulmalarını sınıflandırmak için Hilbert-Huang
Dönüşümü yöntemi ve istatistiksel özellikler ile öznitelikler elde
edilmektedir. Elde edilen özniteliklerden uygun olanları Genetik Algoritma (GA)
k-En Yakın Komşu sınıflandırma yaklaşımı ile seçilmektedir. Yapay Zeka ve
Makine Öğrenmesi yöntemlerine dayalı modeller oluşturulmakta ve deneysel
düzenekten alınan veriler kullanılarak test işlemi yapılmaktadır. Gürültülü
durumlar (40 dB, 30 dB ve 20 dB) ile birlikte matematiksel eşitlikler
kullanılarak üretilmektedir. Bunun yanında deneysel düzenekten elde edilen Güç
Kalitesi Bozulma verisi de bu çalışmada kullanılmaktadır. Sinyallere öncelikle
Ampirik Kip Ayırışımı (EMD) yöntemi uygulanmaktadır. Daha sonra Hilbert
dönüşümü (HT) neticesinde istatistiksel özellikler ile gerekli öznitelikler
çıkartılmaktadır. Aynı işlem Grupsal Ampirik Kip Ayrışımı (EEMD) yöntemi için
tekrarlanmaktadır. Çıkartılan özniteliklerin sayısı itibari ile gerekli
olanlarının seçilebilmesi için GA + KNN sarmalama yaklaşımı kullanılmaktadır.
Çok katmanlı algılayıcı (MLP) ve KNN yaklaşımları ile Güç Kalitesi
Bozulmalarını sınıflandıran modeller oluşturulmaktadır. 9 adet tekli, 9 adet
çoklu bozulma türü için oluşturulan EEMD + HT + GA + KNN sınıflandırma
modelinin başarımı sentetik veriler için %99.15, deneysel veriler için % 99.02
olarak elde edilmektedir. Literatürdeki çalışmalar ile kıyaslandığında elde
edilen EEMD + HT + GA + KNN yönteminin, 9 adet çoklu GK bozulmasını ayırt
edebilme özelliğine sahip olduğu ve %99.12 lik genel başarım oranı ile en iyi
başarımı veren yöntem olduğu sonuçlarına varılmaktadır.

Keywords

Güç kalitesi, Hilbert-Huang Dönüşümü, Ampirik Mod Ayrıştırması (EMD), Grupsal Ampirik Mod Ayrıştırması (EEMD), Genetik Algoritma

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