Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

International Journal of Electrical Power and Energy Systems

Volumn 31, Issue 5, 2009, Pages 206-212

  Oleskovicz, Mário ^a   Coury, Denis V ^a   Felho, Odilon Delmont ^a   Usida, Wesley F ^a   Carneiro, Adriano A F M ^a   Pires, Leandro R S ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Artificial neural networks; Graphical user interface; Hybrid system; Power quality; Wavelet transform

Indexed keywords

ALTERNATIVE TRANSIENTS PROGRAMS; ARTIFICIAL NEURAL NETWORKS; COMPUTATIONAL LOGIC; DISTRIBUTION SYSTEMS; ELECTRICAL POWER SYSTEMS; END USERS; HYBRID APPROACHES; HYBRID METHODOLOGIES; POWER DISTRIBUTION SYSTEMS; POWER QUALITY ANALYSIS; TWO STAGES;

BACKPROPAGATION; ELECTRIC GROUNDING; ELECTRIC LOAD DISTRIBUTION; ELECTRIC MEASURING INSTRUMENTS; GRAPHICAL USER INTERFACES; HYBRID COMPUTERS; HYBRID SYSTEMS; MATHEMATICAL TRANSFORMATIONS; WAVELET TRANSFORMS;

NEURAL NETWORKS;

EID: 67349234185     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2009.01.012     Document Type: Article

References (19)

1. Williams J.R., and Amaratunga K. An introduction to wavelets in engineering. Int J Num Meth Eng 37 (1994) 2365-2388
2. Chui C K. Wavelets: a mathematical tool for signal analysis (1997), SIAM, Philadelphia
3. Daubechies I. Ten lectures on wavelets. CBMS-NSF regional conferences series in applied Mathematics (1992), SIAM, Philadelphia
4. Walker J.S. A primer on wavelets and their scientific applications (1999), Chapman and Hall/CRC, New York
5. Santoso S. Power quality disturbance waveform recognition using wavelet-based neural classifier - Part 1: theoretical foundation. IEEE Trans Power Delivery 15 1 (2000) 222-228
6. Santoso S. Power quality disturbance waveform recognition using wavelet-based neural classifier - Part 2: application. IEEE Trans Power Delivery 15 1 (2000) 229-235
7. Borrás D., Castilla M., Moreno N., and Montano J.C. Wavelet and neural structure: a new tool for diagnostic of power system disturbances. IEEE Trans Industry Appl 37 1 (2001) 184-190
8. Kaewarsa S., Attakitmongcol K., and Kulworawanichpong T. Recognition of power quality events by using multiwavelet-based neural networks. Electric Power Energy Sys 30 (2008) 254-260
9. Huang J., Negnevitsky M., and Nguyen D.T. A neural-fuzzy classifier for recognition of power quality disturbances. IEEE Trans Power Delivery 17 2 (2002) 609-616
10. Al-Kandari A.M., Soliman S.A., and Alammari R.A. Power quality analysis based on fuzzy estimation algorithm: Voltage flicker measurements. Electric Power Energy Sys 28 (2006) 723-728
11. Al-Hasawi W.M., and El-Naggar K.M. A genetic based algorithm for voltage flicker measurement. Electric Power Energy Sys 26 (2004) 593-596
12. Yuan Liao Y., and Lee J.B. A fuzzy-expert system for classifying power quality disturbances. Electric Power Energy Sys 26 (2004) 199-205
13. Ibrahim W.R.A., and Morcos M.M. Artificial intelligence and advanced mathematical tools for power quality applications: a survey. IEEE Trans Power Delivery 17 2 (2002) 668-673
14. Gaing ZL. Implementation of power disturbance classifier using wavelet-based neural networks. In: IEEE Bologna Power Tech Conference; 2003.
15. Alternative Transients Program, Rule Book, Leuven EMTP Center (LEC), ATP;1987.
16. Daubechies I. Where do wavelets come from? - a personal point of view. Proceedings of the IEEE 84 4 (1996) 510-513
17. Kim C.H., and Aggarwal R. Wavelet transforms in power systems: part 1 - general introduction to the wavelet transforms. Power Eng J (2000) 81-87
18. Burrus C.S., Gopinath R.A., and Guo H. Introduction to wavelets and wavelet transforms: a primer (1998), Prentice Hall, New Jersey
19. Delmont Filho O. An algorithm for detection, localization and classification of electrical power quality disturbances using the wavelet transform (in Portuguese), Ph.D. thesis, Electrical Engineering Department, São Carlos Engineering School, University of São Paulo, Brazil, 2007.