| |
 |
Determining Five Kinds of Power Quality Disturbances
by Using Statistical Methods and Wavelet Energy Coefficients
Ç. Kocaman1, M. Özdemir
2017/04/25
|

Abstract
In this paper, it is tried to compare two
methods for determining pure sine and five kinds of power quality disturbances
(PQD) such as voltage sag, voltage swell, voltage with harmonics, transients
and flicker. These methods are statistical methods and wavelet based effective
feature extraction
method. Before classifying power quality signals, one of the feature extraction
method must be applied. So, these two methods are compared. Firstly, statistical
methods are applied to PQD. It is observed that if PQD signal is created
at the zero crossing points of the voltage signal, statistical methods
give satisfactory result. But occurrence of disturbances at these points
is not guaranteed in real systems. It is seen that first method may confuse
some PQD according to occurrence place of disturbances. So second method
is used for extracting the energy distribution features of PQD constituted
in eight different points (00, 450, 900, 1350, 1800, 2250, 2700, 3150).
Parsevals theorem and multi-resolution analysis (MRA) technique
of discrete wavelet technique (DWT) are used. It is observed that this
method gives satisfactory results for eight different points.
| Published in: Renewable Energy
& Power Quality Journal (RE&PQJ, Nº. 15) |
| Pages: 745-750 |
Date of Publication: 2017/04/25 |
| ISSN: 2172-038X |
Date of Current Version: |
|
REF: 455-17
|
Issue Date: April 2017 |
| DOI:10.24084/repqj15.455 |
Publisher: EA4EPQ |
Authors and affiliations
Ç. Kocaman(1), M. Özdemir(2)
1. Department of Aeroplane Maintenance and Repair. Ondokuz Mayýs
University. Campus of Ballýca Ondokuz Mayýs, Samsun
(Turkey)
2. Department of Electrical and Electronic Engineering. Ondokuz Mayýs
University. Campus of Kurupelit Ondokuz Mayýs, Samsun (Turkey)
Key word
Flicker, power quality disturbances, statistical method,
transients, voltage sag, voltage swell, voltage with harmonics.
References
[1] M. Uyar, S. Yýldýrým,
M. T. Gençoðlu, Güç kalitesi bozulmalarýnýn
sýnýflandýrýlmasýnda dalgacýk
dönüþümüyle enerji
daðýlýmýna dayalý özelliklerinin
incelenmesi, in Elektrik Elektronik Biyomedikal Mühendisliði
12. Ulusal Kongre Ve Sergisi, pp.
[2] IEEE Std. 1159-1995 IEEE Recommended Practice for Monitoring Electric
Power Quality, IEEE Standards Coordinating Committee 22 on Power Quality,
USA.
[3] C. Sankaran, Power Quality, CRC Press LLC. 2002, ch 2.
[4] C. Kocatepe, N. Umurkan, F. Atar, R. Yumurtacý, A. Karakaþ,
O. Arýkan, M. Baysal, Elektrik Enerjisi Ve harmonikler Kurs Notlarý,
MÝSEM, 2005, ch. 2.
[5] M. H. J. Bollen, Understanding Power Quality Problems.Chalmers University
of Technology, New York.
[6] W. M. Lin, C. H. Wu, C. H. Lin, F. S. Cheng, Detection and classification
of multiple power quality disturbances with wavelet multiclass SVM,
IEEE Transactions On Power Delivery, vol. 23, pp. 2575-2582, 2008.
[7] A. E. Lazzaretti, V. H. Ferreira, . H. V. Neto, R. J. Riella, J. Omori,
Classification of events in distribution networks using autonomous
neural models, 15th International Conference on Intelligent System
Applications to Power Systems, pp. 1-6.
[8] S. J. Huang, C. T. Hsieh, Feasibility of fractal-based methods
for visualization of power system disturbances, International Journal
of Electrical Power & Energy Systems, vol. 23, pp. 31-36, 2001.
[9] T. Nguyen, Y. Liao, Power quality disturbance classification
utilizing S transformed binary feature matrix method, Elect. Power
Syst.Res., vol. 79, pp. 569-575, 2009.
[10] J. Wen, P. Liu, A method for detection and classification of
power quality disturbances, Automat. Elect. Power Syst., vol. 26,
pp. 42-44, 2002.
[11] S. Santoso, E. J. Powers, W. M. Grady, P. Hofmann, Power quality
assessment via wavelet transform analysis, IEEE Trans. Power Delivery,
vol. 11, pp. 924-930, 1996.
[12] Y. Y. Hong, Y. Y. Chen, Placement of power quality monitors
using enhanced genetic algorithm and wavelet transform, Generation,
Transmission & Distribution, vol. 5, pp. 461-466, 2011.
[13] G. L. David, Comments On Hilbert Transform Based Signal Analysis,
BYU (Microwave Remote Sensing (MERS) Laboratory Technical Report, Brigham
Young University, Provo, UT, 2004.
[14] M. Aiello, A. Cataliotti, S. Nuccio, A chirp-Z transform-based
synchronizer for power system measurements, in 2005 IEEE Trans.Instrument.
Meas. The 19th IEEE Instrumentation and Measurement Technology Conference,
pp. 1025-1032.
[15] Y. Xu, X. Xiangning, Y. H. Song, Automatic classification and
analysis of the characteristic parameters for power quality disturbances,
in 2004 IEEE Power Engineering Society General Meeting, pp. 496-503.
[16] E. Styvaktakis, M. H. J. Bollen, I. Y. H. Gu, Expert system
for classification and analysis of power system events, IEEE Transactions
On Power Delivery, vol. 17, pp. 423-428 , 2002.
[17] D. G. Ece, Güç kalitesi bozucularýnýn
belirlenmesinde dalgacýk dönüþümünün
baþarým sýnamasý, in 2007 ELECO,pp. 12-16.
[18] M. Uyar, S. Yýldýrým, M. T. Gençoðlu,
An effective waveletbased feature extraction method for classification
of power quality disturbance signal, Electric Power System Research,
vol. 78, pp. 1747-1755, 2008.
[19] Z. L. Gaing, H. S. Huang, Wavelet based neural network for
power disturbance classification, in 2003 IEEE

|
|