Power Quality Event Identification Using Higher-Order Statistics and Neural Classifiers
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.
Unable to display preview. Download preview PDF.
- 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
- 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
- 6.Nikias, C.L., Mendel, J.M.: Signal processing with higher-order spectra. IEEE Signal Processing Magazine, 10–37 (1993)Google Scholar
- 8.Nandi, A.K.: Blind Estimation using Higher-Order Statistics, vol. 1, 1st edn. Kluwer Academic Publichers, Boston (1999)Google Scholar
- 10.Nikias, C.L., Petropulu, A.P.: Higher-Order Spectra Analysis. A Non-Linear Signal Processing Framework. Prentice-Hall, Englewood Cliffs (1993)Google Scholar