access icon free Parallel computing for efficient time-frequency feature extraction of power quality disturbances

© The Institution of Engineering and Technology
Received 10/09/2012, Accepted 04/02/2013, Revised 24/12/2012, Published

Fast signal processing implementation techniques for detection and classification of power quality (PQ) disturbances are the need of the hour. Hence in this work, a parallel computing approach has been proposed to speed up the feature extraction of PQ signals to facilitate rapid building of classifier models. Considering that the Fourier, the one-dimensional discrete wavelet, the time-time and the Stockwell transforms have been used extensively to extract pertinent time-frequency features from non-stationary and multi-frequency PQ signals, acceleration approaches using data and task parallelism have been employed for parallel implementation of the above time-frequency transforms. In the first approach, data parallelism was applied to the Stockwell transform and the time-time transform-based feature extraction methods separately to alleviate capability problems. Also, data parallelism was applied to Fourier and wavelet-based feature extraction methods independently to alleviate capacity problems. Secondly, a combination of task and data parallelism was applied to speed up S-transform based three-phase sag feature extraction. Experiments were conducted using shared-memory and distributed memory architectures to try out the effectiveness of the proposed parallel approaches. The performances of these parallel implementations were analysed in terms of computational speed and efficiency in comparison with the sequential approach.

Inspec keywords: parallel memories; signal classification; distributed memory systems; feature extraction; power system faults; power supply quality; power engineering computing; Fourier transforms; time-frequency analysis; parallel processing; discrete wavelet transforms; signal detection; shared memory systems

Other keywords: Fourier transform; nonstationary PQ signal; Stockwell transform; task parallelism; time-time transform; acceleration approach; shared memory architecture; sag feature extraction; classifier model; power quality disturbance classification; parallel computing; data parallelism; signal processing; distributed memory architecture; power quality disturbance detection; time-frequency transform; multifrequency PQ signal; 1D discrete wavelet transform; S-transform; time-frequency feature extraction

Subjects: Multiprocessing systems; Integral transforms; Parallel architecture; Parallel software; Power engineering computing; Integral transforms; Power supply quality and harmonics; Signal detection

References

    1. 1)
      • 11. Chilukuri, M.V., Dash, P.K.: ‘Multiresolution s-transform-based fuzzy recognition system for power quality events’, IEEE Trans. Power Deliv., 2004, 19, (1), pp. 323–329 (doi: 10.1109/TPWRD.2003.820180).
    2. 2)
      • 7. Gaouda, A.M., Salama, M.M.A., Sultan, M.R., Chikhani, A.Y.: ‘Power quality detection and classification using wavelet-multiresolution signal decomposition’, IEEE Trans. Power Deliv., 1999, 14, (4), pp. 1469–1476 (doi: 10.1109/61.796242).
    3. 3)
      • 2. Axelberg, P.G.V., Gu, I.Y.J., Bollen, M.H.J.: ‘Support vector machine for classification of voltage disturbances’, IEEE Trans. Power Deliv., 2007, 22, (3), pp. 1297–1303 (doi: 10.1109/TPWRD.2007.900065).
    4. 4)
      • 15. Suja, S., Jerome, J.: ‘Pattern recognition of power signal disturbances using s-transform and tt transform’, Int. J. Elec. Power, 2010, 32, (1), pp. 37–53 (doi: 10.1016/j.ijepes.2009.06.012).
    5. 5)
      • 21. Hasheminejad, S., Esmaeili, S., Gharaveisi, A.A.: ‘Three phase power quality disturbance classification using S-transform’, Aust. J. Basic Appl. Sci., 2010, 4, (2), pp. 6547–6563.
    6. 6)
      • 6. Gaing, Z.L.: ‘Wavelet-based neural network for power disturbance recognition and classification’, IEEE Trans. Power Deliv., 2004, 19, (4), pp. 1560–1568 (doi: 10.1109/TPWRD.2004.835281).
    7. 7)
      • 16. Samsi, S., Gadepally, V., Krishnamurthy, V.: ‘MATLAB for signal processing on multiprocessors and multicores’, IEEE Signal Process. Mag., 2010, 27, (2), pp. 40–49 (doi: 10.1109/MSP.2009.935421).
    8. 8)
      • 12. Lee, C.Y., Shen, Y.X.: ‘Optimal feature selection for power-quality disturbances classification’, IEEE Trans. Power Deliv., 2011, 26, (4), pp. 2342–2351 (doi: 10.1109/TPWRD.2011.2149547).
    9. 9)
      • 5. Gu, I.Y.H., Styvaktakis, E.: ‘Bridge the gap: signal processing for power quality applications’, Electr. Power Syst. Res., 2003, 66, (1), pp. 83–96 (doi: 10.1016/S0378-7796(03)00074-9).
    10. 10)
      • 18. Gao, W., Kemao, Q., Wang, H., Lin, F., Seah, H.: ‘Parallel computing for fringe pattern processing: a multicore CPU approach in MATLABs environment’, Opt. Laser. Eng., 2009, 47, pp. 1286–1292 (doi: 10.1016/j.optlaseng.2009.04.018).
    11. 11)
      • 19. http://www.mathworks.in/products/distriben/requirements.html, accessed December 2012.
    12. 12)
      • 20. Dugan, R.C., McGranaghan, M.F., Santaso, S., Wayne Beaty, H.: ‘Electrical power systems quality’ (McGraw Hill, 2002, 2nd edn.), pp. 52.
    13. 13)
      • 10. Faisal, M.F., Mohamed, A., Shareef, H., Hussain, A.: ‘Power quality diagnosis using time frequency analysis and rule based techniques’, Expert Syst. Appl., 2011, 38, (10), pp. 12592–12598 (doi: 10.1016/j.eswa.2011.04.047).
    14. 14)
      • 13. Biswal, B., Dash, P.K., Panigrahi, B.K.: ‘Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization’, IEEE Trans. Ind. Electron, 2009, 56, (1), pp. 212–220 (doi: 10.1109/TIE.2008.928111).
    15. 15)
      • 17. Stockwell, R.G., Mansinha, L., Lowe, R.P.: ‘Localization of the complex spectrum: the S transform’, IEEE Trans. Signal Process, 1996, 44, (4), pp. 998–1001 (doi: 10.1109/78.492555).
    16. 16)
      • 1. Nguyen, T., Liao, Y.: ‘Power quality disturbance classification utilizing S-transform and binary feature matrix method’, Electr. Power Syst. Res., 2009, 79, (4), pp. 569–575 (doi: 10.1016/j.epsr.2008.08.007).
    17. 17)
      • 4. Youssef, A.M., Abdel-Galil, T.K., El-Saadany, E.F., Salama, M.M.A.: ‘Disturbance classification utilizing dynamic time warping classifier’, IEEE Trans. Power Deliv, 2004, 19, (1), pp. 272–278 (doi: 10.1109/TPWRD.2003.820178).
    18. 18)
      • 3. Ribeiro, M.V., Pereira, J.L.R.: ‘Classification of single and multiple disturbances in electric signals’, EURASIP J. Adv. Signal Process, 2007, article id 56918, 18 pages, doi: 10.1155/2007/56918.
    19. 19)
      • 14. Biswal, B., Dash, P.K., Mishra, S.: ‘A hybrid ant colony optimization technique for power signal pattern classification’, Expert Syst. Appl., 2011, 38, (5), pp. 6368–6375 (doi: 10.1016/j.eswa.2010.11.102).
    20. 20)
      • 9. Faisal, M.F., Mohamed, A.: ‘Identification of multiple power quality disturbances using s-transform and rule based classification technique’, J. Appl. Sci. Res., 2009, 9, (15), pp. 2688–2700 (doi: 10.3923/jas.2009.2688.2700).
    21. 21)
      • 8. Fengzhan, Z., Rengang, Y.: ‘Power quality disturbance recognition using s-transform’, IEEE Trans. Power Deliv, 2007, 22, (2), pp. 944–950 (doi: 10.1109/TPWRD.2006.881575).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2012.0262
Loading

Related content

content/journals/10.1049/iet-spr.2012.0262
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading