Analysing the Effect of Metaheuristic Based Feature Selection Algorithms on the Classification of the Power Quality Events

Year 2024, Volume: 16 Issue: 2, 646 - 658, 30.06.2024

Birsen Gümüş , Melih Çoban , Suleyman Sungur Tezcan

https://doi.org/10.29137/umagd.1423997

Abstract

In this study, two different optimization algorithms have been used for the feature selection stage, which plays a crucial role in the classification of power quality disturbances. In the first part of the study, signals containing power quality events were generated to initiate the classification process. Discrete wavelet transform (DWT) has been used for feature extraction. Two different datasets were obtained by normalizing and taking logarithm of the dataset obtained after the feature extraction process. The Equilibrium Optimizer (EO) and the Salp Swarm Optimization Algorithm (SSA), which are named metaheuristic based feature selection algorithms, were used for the feature selection process. The K Nearest Neighbour Algorithm (KNN) is preferred for classification. The highest accuracy rate in classification was achieved at 96.05% when utilizing EO as the feature selection algorithm and using the logarithmic dataset. The lowest classification accuracy rate was obtained as 90.62% when the feature selection algorithm was SSA and the normalized data set was used. In the second part of the study, a histogram graph was created to identify the most frequently selected features from the first part. The highest accuracy rate obtained for the classification using the histogram graph was observed to be 95.8% and the lowest accuracy rate was 93.83%.

Keywords

Power quality events, Classification, Machine Learning

Metasezgisel Tabanlı Özellik Seçim Algoritmalarının Güç Kalitesi Olaylarının Sınıflandırılmasına Etkisinin İncelenmesi

Abstract

Bu çalışmada, güç kalitesi bozulmalarının sınıflandırmasında önemli bir role sahip olan özellik seçme aşaması için iki farklı optimizasyon algoritması kullanılmıştır. Çalışmanın birinci kısmında, sınıflandırma sürecinin başlaması için güç kalitesi olaylarını içeren sinyaller üretilmiştir. Özellik çıkarma için Ayrık Dalgacık Dönüşümü (DWT) kullanılmıştır. Özellik çıkarma işleminden sonra elde edilen veri seti, normalize edilerek ve logaritması alınarak iki farklı veri seti elde edilmiştir Özellik seçme işlemi için Denge Optimize Edici Algoritması (EO) ve Salp Sürü Optimizasyon Algoritması (SSA) olarak isimlendirilen metasezgisel tabanlı optimizasyon algoritmaları özellik seçme algoritmaları olarak kullanılmıştır. Sınıflandırma için K En Yakın Komşu Algoritması (KNN) tercih edilmiştir. En yüksek sınıflandırma doğruluk oranı, özellik seçme algoritması olarak EO ve veri seti olarak logaritmik veri setinin kullanıldığı durumda, %96.05 olarak elde edilmiştir. En düşük sınıflandırma doğruluk oranı ise özellik seçme algoritmasının SSA olduğu ve normalize veri setinin kullanıldığı durumda, % 90.62 olarak elde edilmiştir. Çalışmanın ikinci kısmında ise, birinci kısımda en çok seçilen özellikleri tespit etmek için histogram grafiği oluşturulmuştur. En çok seçilen sekiz özellik ile sınıflandırma işlemi tekrarlanmıştır. Histogram grafiği kullanılarak yapılan sınıflandırmaya ilişkin elde edilen en yüksek doğruluk oranı % 95.8 ve en düşük doğruluk oranı % 93.83 olarak gözlenmiştir.

Keywords

Güç Kalitesi olayları, Sınıflandırma, Makine Öğrenmesi

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