Classification of power quality disturbances using S-transform and TT-transform based on the artificial neural network

Authors : Sajad JASHFAR, Saeid ESMAEILI, Mehdi ZAREIAN-JAHROMI

Pages : 1528-1538

Doi:10.3906/elk-1112-51

View : 10 | Download : 7

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :The classification of power quality insert ignore into journalissuearticles values(PQ); disturbances to improve the PQ is an important issue in utilities and industrial factories. In this paper, an approach to classify PQ disturbances is presented. First, a signal containing one of the PQ disturbances, like sag, swell, flicker, interruption, transient, or harmonics, is evaluated using the proposed approach. Afterwards, S-transform and TT-transform are applied to the signal and an artificial neural network is used to recognize the disturbance using S-transform and TT-transform data, like the variance and mean values of S-transform and TT-transform matrices. The main features of the proposed approach are the real-time and very fast recognition of the PQ disturbances. Finally, the proposed method`s results are compared with the support vector machine and k-nearest neighbor classification methods to verify the results. The results show the effectiveness of this state-of-the-art approach.
Keywords : Power quality, disturbances, short time Fourier transform, S transform, TT transform, artificial neural network