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

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.

Anahtar Kelimeler:

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

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

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,

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