Power Quality Event Identification Using Higher-Order Statistics and Neural Classifiers

  • Juan-José González de-la-Rosa
  • Carlos G. Puntonet
  • Antonio Moreno Muñoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


This paper deals with power-quality (PQ) event detection, classification and characterization using higher-order sliding cumulants to examine the signals. Their maxima and minima are the main features, and the classification strategy is based in competitive layers. Concretely, we concentrate on the task of differentiating two types of transients (short duration and long duration). By measuring the fourth-order central cumulants’ maxima and minima, we build the two-dimensional feature measured vector. Cumulants are calculated over high-pass digitally filtered signals, to avoid the low-frequency 50-Hz signal. We have observed that the minima and maxima measurements produce clusters in the feature space for 4th-order cumulants; third-order cumulants are not capable of differentiate these two very similar PQ events. The experience aims to set the foundations of an automatic procedure for PQ event detection.


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  1. 1.
    Moreno, A., Pallarés, V., de la Rosa, J.J.G., Galisteo, P.: Study of voltage sag in a highlty automated plant. In: MELECON 2006, Proceedings of the 2006 13th IEEE Mediterranean Electrotechnical Conference (2006)Google Scholar
  2. 2.
    Gerek, Ö.N., Ece, D.G.: Power-quality event analysis using higher order cumulants and quadratic classifiers. IEEE Transactions on Power Delivery 21, 883–889 (2006)CrossRefGoogle Scholar
  3. 3.
    de la Rosa, J.J.G., Puntonet, C.G., Lloret, I., Górriz, J.M.: Wavelets and wavelet packets applied to termite detection. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 900–907. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    de la Rosa, J.J.G., Ruzzante, R.P.J.: Third-order spectral characterization of acoustic emission signals in ring-type samples from steel pipes for the oil industry. Mechanical systems and Signal Processing 21, 1917–1926 (2007), Available online 10 October 2006CrossRefGoogle Scholar
  5. 5.
    de la Rosa, J.J.G., Lloret, I., Puntonet, C.G., Górriz, J.M.: Higher-order statistics to detect and characterise termite emissions. Electronics Letters 40(20), 1316–1317 (2004)CrossRefGoogle Scholar
  6. 6.
    Nikias, C.L., Mendel, J.M.: Signal processing with higher-order spectra. IEEE Signal Processing Magazine, 10–37 (1993)Google Scholar
  7. 7.
    Mendel, J.M.: Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications. Proceedings of the IEEE 79, 278–305 (1991)CrossRefGoogle Scholar
  8. 8.
    Nandi, A.K.: Blind Estimation using Higher-Order Statistics, vol. 1, 1st edn. Kluwer Academic Publichers, Boston (1999)Google Scholar
  9. 9.
    de la Rosa, J.J.G., Puntonet, C.G., Lloret, I.: An application of the independent component analysis to monitor acoustic emission signals generated by termite activity in wood. Measurement 37, 63–76 (2005), Available online 12 October 2004CrossRefGoogle Scholar
  10. 10.
    Nikias, C.L., Petropulu, A.P.: Higher-Order Spectra Analysis. A Non-Linear Signal Processing Framework. Prentice-Hall, Englewood Cliffs (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Juan-José González de-la-Rosa
    • 1
    • 3
  • Carlos G. Puntonet
    • 2
  • Antonio Moreno Muñoz
    • 3
    • 4
  1. 1.University of Cádiz, Electronics Area, EPSA, Av. Ramón Puyol S/N. E-11202, Algeciras-CádizSpain
  2. 2.University of Granada, Dept. of Architecture and Computers Technology, ESII, C/Periodista Daniel Saucedo. 18071, GranadaSpain
  3. 3.Research Group TIC168-Computational Instrumentation and Industrial Electronics 
  4. 4.University of Córdoba, Electronics Area, Escuela Pol. Superior, Campus Rabanales, CórdobaSpain

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