Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm
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.1033182Özet
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
Kaynak
NATURENGSCilt
3Sayı
1Bağlantı
https://dergipark.org.tr/tr/download/article-file/2117997https://hdl.handle.net/20.500.12899/1192