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

نویسندگانSajad Jashfar- SAEED ESMAEILI- MEHDI ZAREIAN JAHROMI- Mohsen Rahmanian
نشریهTurkish Journal of Electrical Engineering & Computer Sciences
شماره صفحات1528-1538
شماره سریال6
شماره مجلد21
نوع مقالهFull Paper
تاریخ انتشار2013-10-28
رتبه نشریهISI
نوع نشریهچاپی
کشور محل چاپترکیه

چکیده مقاله

The classification of power quality (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.