Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
MTÜ
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, a method based on Stockwell transform (ST), ReliefF feature selection method and Multilayer Perceptron Algorithm (MPA) algorithm was developed for classification of Power Quality (PQ) disturbance signals. First of all, ST was applied to different PQ signals to obtain classification features in the method. Then, total of 30 different classification features were obtained by taking different entropy values of the matrix obtained after ST and different entropy values of the PQ signals. The use of all of the classification features obtained causes the method to be complicated and the training/testing times to be prolonged. Therefore, so as to determine the effective ones among the classification features and to ensure high classification success with less classification features, ReliefF feature selection method was used in this study. PQ disturbances were classified by using 8 different classification features determined by ReliefF feature selection method and MPA. The simulation results show that the method provides a high classification success in a shorter training/testing time. At the same time, simulation results have shown that the method was successful on testing data with noise levels of 35 dB and above after only one training
Açıklama
Anahtar Kelimeler
Classification, Multilayer perceptron algorithm, Power quality, Relief feature selection, S-transform.
Kaynak
NATURENGS
WoS Q Değeri
Scopus Q Değeri
Cilt
3
Sayı
1
Künye
Akmaz, D. (2022). Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm . NATURENGS , 3 (1) , 13-23 . DOI: 10.46572/naturengs.1033182
