Classification of Power Quality Disturbances with S-Transform and Artificial Neural Networks Method

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYWOS: 000413813100080In this study, classification of 11 different Power Quality (PQ) disturbances with Artificial Neural Networks (ANN) has been done by using the attributes obtained with S-Transform. It was aimed to achieve accurate and high classification performance in noisy environment by using the least number of attributes representing PQ disturbances. The most suitable ones from the attributes were selected by Sequential Forward Selection (SFS) method. The performance of models with different hidden layer neuron numbers tested at different noise levels (40 dB, 30 dB and 20 dB) by using the selected attributes. In this study, it was found that for the most appropriate number of attributes and optimal model parameters, performance in noisy environment (20 dB) and overall performance were 99.0%.Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Uni

Classification of power quality disturbances with S-transform and artificial neural networks method [S-Dönüşümü ve Yapay Sinir Aglari Yöntemi ile Gç Kalitesi Bozulmalarinin Siniflandirilmasi]

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703In this study, classification of 11 different Power Quality (PQ) disturbances with Artificial Neural Networks (ANN) has been done by using the attributes obtained with S-Transform. It was aimed to achieve accurate and high classification performance in noisy environment by using the least number of attributes representing PQ disturbances. The most suitable ones from the attributes were selected by Sequential Forward Selection (SFS) method. The performance of models with different hidden layer neuron numbers tested at different noise levels (40 dB, 30 dB and 20 dB) by using the selected attributes. In this study, it was found that for the most appropriate number of attributes and optimal model parameters, performance in noisy environment (20 dB) and overall performance were 99.0%. © 2017 IEEE