Amplitude-Frequency Monitoring of Power Quality Transients using Higher-Order Statistics and Self-Organizing Neural Networks

• Autorzy: Gonzalez de la Rosa J. J. , Aguera-Perez A. , Palomares-Salas J. C. , Moreno-Munoz A.

Warianty tytułu

PL

Monitorowanie jakości energii w stanach przejściowych przy użyciu statystyki wyższego rządu i sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

In this paper a smart automatic classification of PQ transients is performed attending to their amplitudes and frequencies, and the extreme of higher-order cumulants. Feature extraction stage is double folded. First, these statistical measurements reveal the hidden geometry for a constant amplitude or frequency, conforming the 2D clustering grace to the third and fourth-order features associated to each signal anomaly, coupled to the 50-Hz power line. Precisely the main contribution of the work is the novel finding that the maxima and the minima of the higher-order cumulants distribute according to curves families, each of which associated to the transient's frequency or amplitude. Given a statistical order, each datum in a curve corresponds to the initial amplitude (or constant frequency), and to a couple of extremes (min-max) associated to the statistical estimator. The random grouping along each curve reveals the a priori hidden geometry, linked to the subjacent electrical phenomenon. Secondly, the regular surface grid in the input space (amplitude-frequency) experiments a transformation to the output space which is developed by the higher-order statistics. Once the geometry in the feature space has been found, we show the computational intelligence modulus, based in Self-Organizing Maps, which performs satisfactory learning along each frequency and amplitude curve. Performance of a four-neuron network with different geometries is shown, confirming the curves' patterns.

PL

W artykule opisano automatyczną metodę klasyfikacji jakości energii w stanach przejściowych z uwzględnieniem amplitudy, częstotliwości i wartości ekstremalnych. W pierwszym etapie przeprowadzane są pomiary statystyczne dla stałej amplitudy i częstotliwości uwzględniające klastry 2D i właściwości trzeciego i czwartego rzędu towarzyszące anomaliom. Następnie uwzględniana jest geometria sieci. Po tym etapie włączany jest moduł sztucznej inteligencji bazujący na sieciach neuronowych.

Słowa kluczowe

EN

higher-order statistics; HOS; Artificial Neural Networks; ANN; Power-Qality; PQ; self-organizing map; SOM; transients

PL

jakość energii; sztuczne sieci neuronowe; statystyka wyższego rzędu

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2011
• Tom: R. 87, nr 8
• Strony: 128--137
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Gonzalez de la Rosa J. J.

autor

Aguera-Perez A.

autor

Palomares-Salas J. C.

autor

Moreno-Munoz A.

• Univ. of Cadiz. EPSA. Av. Ramon Puyol S/N. E-11202-Algeciras-Cadiz-Spain

Bibliografia

• [1] M. H. J. Bollen, I. Y.-H. Gu, P. G. V. Axelberg, E. Styvaktakis, Classification of underlying causes of power quality disturbances: Deterministic versus statistical methods, EURASIP Journal on Advances in Signal Processing 2007 (Article ID- 79747) (2007) 1–17.
• [2] A. Moreno, et al, Mitigation Technologies in a Distributed Environment, 1st Edition, Power Systems, Springer-Verlag, 2007.
• [3] M. Riera-Guasp, J. A. Antonino-Daviu, M. Pineda-Sanchez, R. Puche-Panadero, J. Perez-Cruz, A general approach for the transient detection of slip-dependent fault components based on the discrete wavelet transform, IEEE Transactions on Industrial Electronics 55 (12) (2008) 4167–4180.
• [4] M. V. Ribeiro, C. A. G. Marques, C. A. Duque, A. S. Cerqueira, J. L. R. Pereira, Detection of disturbances in voltage signals for power quality analysis using hos, EURASIP Journal on Advances in Signal Processing 2007 (Article ID-59786) (2007) 1– 13.
• [5] J. J. G. De la Rosa, A. M. Munoz, A. Gallego, R. Piotrkowski, E. Castro, Higher-order characterization of power quality transients and their classification using competitive layers, Measurement (Ed. Elsevier) 42 (Issue 3) (2009) 478–484.
• [6] J. J. G. De la Rosa, A. Moreno, C. G. Puntonet, A practical review on higher-order statistics interpretation. application to electrical transients characterization, Dynamics of continous discrete and Impulsive Systems-Series B: Applications and Algorithms 14 (4) (2007) 1577–1582.
• [7] Omer Nezih Gerek, D. G. Ece, Power-quality event analysis using higher order cumulants and quadratic classifiers, IEEE Transactions on Power Delivery 21 (2) (2006) 883–889.
• [8] J. J. G. De la Rosa, I. Lloret, C. G. Puntonet, J. M. Gorriz, Higher-order statistics to detect and characterise termite emissions, Electronics Letters 40 (20) (2004) 1316–1317, Ultrasonics.
• [9] J. J. G. De la Rosa, I. Lloret, C. G. Puntonet, R. Piotrkowski, A. Moreno, Higher-order spectra measurement techniques of termite emissions. a characterization framework, Measurement (Ed. Elsevier) 41 (1) (2008) 105–118, available online 13 October 2006.
• [10] J. J. G. De la Rosa, R. Piotrkowski, J. Ruzzante, Third-order spectral characterization of acoustic emission signals in ringtype samples from steel pipes for the oil industry, Mechanical Systems and Signal Processing (Ed. Elsevier) 21 (Issue 4) (2007) 1917–1926, available online 10 October 2006.
• [11] J. J. G. De la Rosa, A. M. Munoz, Higher-order characterization of power quality transients and their classification using competitive layers, Przegląd Elektrotechniczny-Electrical Review 10 (Issue 85) (2009) 284–289.
• [12] M. H. J. Bollen, E. Styvaktakis, I. Y.-H. Gu, Categorization and analysis of power system transients, IEEE Transactions on Power Delivery 20 (3) (2005) 105–118.
• [13] D. Paul, Low-voltage power system surge overvoltage protection, IEEE Transactions on Industry Applications 37 (1) (2001) 223–229.
• [14] F. Martzloff, Protecting computer systems against power transients, IEEE Spectrum 27 (Issue 4) (1990) 37–40.
• [15] IEEE Recommended practice for monitoring electric power quality, Tech. Rep. IEEE Std. 1159-1995, The Institute of Electrical and Electronics Engineers, Inc. (1995).
• [16] IEEE Guide for service to equipment sensitive to momentary voltage disturbances, Tech. Rep. IEEE Std. 1250-1995, The Institute of Electrical and Electronics Engineers, Inc. (April) 1995).
• [17] A. Moreno, J. Flores, D. Oterino, J. J. G. De la Rosa, Power line conditioner based on ca pwm chopper, in: ISIE 2007, Proceedings of the 2007 IEEE International Symposium on Industrial Electronics, June 2007, 2007, pp. 2454–2456.
• [18] C. L. Nikias, A. P. Petropulu, Higher-Order Spectra Analysis. A Non-Linear Signal Processing Framework, Englewood Cliffs, NJ, Prentice-Hall, 1993.
• [19] C. L. Nikias, J. M. Mendel, Signal processing with higher-order spectra, IEEE Signal Processing Magazine (1993) 10–37.
• [20] J. M. Mendel, Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications, Proceedings of the IEEE 79 (3) (1991) 278– 305.
• [21] A. K. Nandi, Blind Estimation using Higher-Order Statistics, 1st Edition, Vol. 1, Kluwer Academic Publishers, Boston, 1999.