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

Sajad JASHFAR; Saeid ESMAEILI; Mehdi ZAREIAN-JAHROMI

Makale detayları

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

Yazarlar:

Sajad JASHFAR | Saeid ESMAEILI | Mehdi ZAREIAN-JAHROMI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 6, pp.1528-1538

Belge Tipi:

Makale

Yayın Tarihi:

5/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743143

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

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.

 

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.

Makale detayları

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

Yazarlar: Sajad JASHFAR | Saeid ESMAEILI | Mehdi ZAREIAN-JAHROMI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 6, pp.1528-1538

Belge Tipi: Makale

Yayın Tarihi: 5/2013

Belge Dili: Türkçe

Document ID (Org No.): 2000743143

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Sajad JASHFAR | Saeid ESMAEILI | Mehdi ZAREIAN-JAHROMI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 6, pp.1528-1538

Belge Tipi:

Makale

Yayın Tarihi:

5/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743143

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632