Application of Two Dimensional Wavelet Transform for Classification of Power Quality Disturbances

• Autorzy: Mollayi N. , Mokhtari H.
• Języki publikacji: EN

Abstrakty

EN

Identification of voltage and current disturbances is an important task in power system monitoring and protection. In this paper, the application of two-dimensional wavelet transform for characterization of a wide variety range of power quality disturbances is discussed, and a new algorithm, based on image processing techniques is proposed for this purpose. A matrix is formed based on a specified number of cycles in such a way that the samples of voltage signal in each cycle form one row of that matrix. This matrix can be regarded as a two dimensional image. A two-dimensional wavelet transform is used to decompose the image into approximation and details, which contain low frequency and high frequency components along the rows and columns, respectively. Different disturbances result into different special patterns in detail images. By processing the detail images, specific features are defined which can suitably discriminate various types of disturbances. Combination of the feature generation algorithm and a classifier system leads to a smart system for classification of wide variety range of disturbances.

Słowa kluczowe

EN

power quality; event detection and classification; two dimensional wavelet transform; pattern classification; image processing; feature; classifier system

• Wydawca: AGH University of Science and Technology ; Lodz University of Technology
• Czasopismo: Electrical Power Quality and Utilisation. Journal
• Rocznik: 2014
• Tom: Vol. 17, iss. 2
• Strony: 1--7
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Mollayi N.

• Department of Computer Engineering and IT, Birjand University of Technology, Birjand, Iran

autor

Mokhtari H.

• Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran

Bibliografia

• 1. Acharya T. and A. Ray : Image Processing: Principles and Applications. John Wiley & Sons Inc, 2005, chapter 5.
• 2. Adil W.A., Keerio M.U. and A.P. Memon: Investigation of suitable Mother Wavelet Transform Functions for Detection of Power System Transient Disturbances. Proceedings of the First International Conference on Modern Communication & Computing Technologies, Nawabshah, Pakistan, Feb. 26-28, 2014.
• 3. Antoine J.-P., Murenzi R., Vandergheynst P. and S.T. Ali: Two-dimensional wavelets and their relatives. Cambridge University Press, Cambridge 2004, chapter 10.
• 4. Bollen M. and I.Gu : Signal Processing of Power Quality Disturbances. John Wiley & Sons Inc: IEEE Press, 2006.
• 5. Bow S . T. : Pattern recognition and image preprocessing. CRC Press, 2002, chapters 1, 8, 15.
• 6. Daubeshies I .: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics Philadelphia, 1992, pp. 1-102.
• 7. Deokar S.A. and L.M. Waghmare : Integrated DWT–FFT approach for detection and classification of power quality disturbances. International Journal of Electrical Power and Energy Systems, Vol. 61 (2014), Oct., pp. 594-605.
• 8. Duda R., Hart P. and D. Stork: Pattern Classification. Wiley-Interscience Press, 2000, chapter 1.
• 9. Dugan D . C . M.F. McGran aghan : Electrical Power Systems Quality. 2nd ed., McGrawHill, 2004, pp. 1-41.
• 10. Ece D. G. and Ö. N. Gerek: Power Quality Event Detection Using Joint 2-D-W avelet Subspaces. IEEE Trans. Instrumentation and Measurement, Vol. 53 (2004), Iss. 4, pp. 1040- 1046.
• 11. Gerek Ö. N. and D. G. Ece: 2-D analysis and compression of power quality event data. IEEE Trans. Power Delivery, Vol. 19 (2004), Iss. 2, pp. 791-798.
• 12. IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE Standard 1159, 1995.
• 13. Karimi M., Mokhtari H. and M. R.Iravani : Experimental Performance Evaluation of a Wavelet-Based On-Line Voltage Detection Method for Power Quality Applications. IEEE Trans. Power Delivery, Vol. 17 (2002), Iss. 1, pp. 161-172.
• 14. Karimi M., Mokhtari H. and M. R.Iravanii : Wavelet based on-line disturbance detection for power quality applications. IEEE Trans. Power Delivery, Vol. 15 (2000), Iss. 4, pp. 1212-1220.
• 15. Kong T. and A. Rosenfeld : Topological algorithms for digital image processing. Elsevier Science, 1996, chapter 1.
• 16.Kusko A. and M. Thompson : Power Quality in Electrical Systems. McGrawHill, 2007, pp. 1-15.
• 17. Mollayi N. and H. Mokhtari : Classification of Power Quality Events Based on Two Dimensional Wavelet Transformation. Proceedings of the 24th International Power Systems Conference, Teheran, Nov. 16-19, 2009.
• 18. Mollayi N. and H. Mokhtari : Classification of Wide Variety range of Power Quality Disturbances Based on Two Dimensional Wavelet Transformation. Proceedings of the 1st Power Electronic & Drive Systems & Technologies Conference, pp. 398-405, Teheran, Feb. 17-18, 2010.
• 19. Oppenheim A. V., Schafer R. W. and J . R. Buck: Discrete Time Signal Processing. Prentice Hall, 1998, pp. 140-213, 541-669.
• 20. Robertson D. C., Camps O. I., Mayer J. S. and W. B. Gish : Wavelets and electromagnetic power system transients. IEEE Trans. Power Delivery, Vol. 11 (1996), Iss. 2, pp. 1050-1058.
• 21. Sankaran C .: Power Quality. CRC Press, 2002, pp. 12-24.
• 22. Theodoridis S. and K. Koutroumbas : Pattern Recognition. 3rd ed., San Diego, CA: Academic Press, 1996, chapter 1.
• 23. Tzanakou E. M .: Supervised and unsupervised pattern recognition: feature extraction and computational intelligence. CRC Press, 1999, chapters 1, 2.
• 24. Wilson J. N. and G. X.Ritter : Handbook of computer vision algorithms in image algebra. CRC Press, 2010, chapter 6.