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  • Research Article
  • Open Access

Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS

  • 1Email author,
  • 1,
  • 1,
  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20072007:059786

https://doi.org/10.1155/2007/59786

  • Received: 1 May 2006
  • Accepted: 4 February 2007
  • Published:

Abstract

This paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds to, at least, samples or of the fundamental component if a sampling rate equal to Hz is considered. This feature allows the detection of disturbances in submultiples or multiples of one-cycle fundamental component if an appropriate sampling rate is considered. From the computational results, one can note that almost all abnormal and normal conditions are correctly detected if s256, 128, 64, 32, and 16 and the SNR is higher than 25 dB. In addition, the proposed technique is compared to a root mean square (rms)-based technique, which was recently developed to detect the presence of some voltage events as well as their sources in a frame whose length ranges from up to one-cycle fundamental component. The numerical results reveal that the proposed technique shows an improved performance when applied not only to synthetic data, but also to real one.

Keywords

  • Information Technology
  • Quantum Information
  • Quality Analysis
  • Voltage Signal
  • Power Quality

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Authors’ Affiliations

(1)
Department of Electrical Circuit, Federal University of Juiz de Fora, Juiz de Fora, MG, 36 036 330, Brazil
(2)
Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora, MG, 36 036 330, Brazil

References

  1. Ribeiro MV: Signal processing techniques for power line communication and power quality applications, Ph.D. dissertation. University of Campinas (UNICAMP), São Paulo, Brasil, April 2005.Google Scholar
  2. Duque CA, Ribeiro MV, Ramos FR, Szczupak J: Power quality event detection based on the divide and conquer principle and innovation concept. IEEE Transactions on Power Delivery 2005,20(4):2361-2369. 10.1109/TPWRD.2005.855478View ArticleGoogle Scholar
  3. Gu IYH, Ernberg N, Styvaktakis E, Bollen MHJ: A statistical-based sequential method for fast online detection of fault-induced voltage dips. IEEE Transactions on Power Delivery 2004,19(2):497-504. 10.1109/TPWRD.2003.823199View ArticleGoogle Scholar
  4. Karimi M, Mokhtari H, Iravani MR: Wavelet based on-line disturbance detection for power quality applications. IEEE Transactions on Power Delivery 2000,15(4):1212-1220. 10.1109/61.891505View ArticleGoogle Scholar
  5. Mokhtari H, Karimi-Ghartemani M, Iravani MR: Experimental performance evaluation of a wavelet-based on-line voltage detection method for power quality applications. IEEE Transactions on Power Delivery 2002,17(1):161-172. 10.1109/61.974204View ArticleGoogle Scholar
  6. Ece DG, Gerek ON: Power quality event detection using joint 2-D-wavelet subspaces. IEEE Transactions on Instrumentation and Measurement 2004,53(4):1040-1046. 10.1109/TIM.2004.831137View ArticleGoogle Scholar
  7. Lu C-W, Huang S-J: An application of B-spline wavelet transform for notch detection enhancement. IEEE Transactions on Power Delivery 2004,19(3):1419-1425. 10.1109/TPWRD.2004.829131View ArticleGoogle Scholar
  8. Abdel-Galil TK, El-Saadany EF, Salama MMA: Power quality event detection using Adaline. Electric Power Systems Research 2003,64(2):137-144. 10.1016/S0378-7796(02)00173-6View ArticleGoogle Scholar
  9. Dash PK, Chilukuri MV: Hybrid S-transform and Kalman filtering approach for detection and measurement of short duration disturbances in power networks. IEEE Transactions on Instrumentation and Measurement 2004,53(2):588-596. 10.1109/TIM.2003.820486View ArticleGoogle Scholar
  10. Gu IY-H, Styvaktakis E: Bridge the gap: signal processing for power quality applications. Electric Power Systems Research 2003,66(1):83-96. 10.1016/S0378-7796(03)00074-9View ArticleGoogle Scholar
  11. Zhang H, Liu P, Malik OP: Detection and classification of power quality disturbances in noisy conditions. IEE Proceedings: Generation, Transmission and Distribution 2003,150(5):567-572. 10.1049/ip-gtd:20030459Google Scholar
  12. Huang S-J, Yang T-M, Huang J-T: FPGA realization of wavelet transform for detection of electric power system disturbances. IEEE Transactions on Power Delivery 2002,17(2):388-394. 10.1109/61.997905View ArticleGoogle Scholar
  13. Castaldo D, Gallo D, Landi C, Testa A: A digital instrument for nonstationary disturbance analysis in power lines. IEEE Transactions on Instrumentation and Measurement 2004,53(5):1353-1361. 10.1109/TIM.2004.830596View ArticleGoogle Scholar
  14. Yang H-T, Liao C-C: A de-noising scheme for enhancing wavelet-based power quality monitoring system. IEEE Transactions on Power Delivery 2001,16(3):353-360. 10.1109/61.924810MathSciNetView ArticleGoogle Scholar
  15. Anderson BDO, Moore JB: Optimal Filtering. Prentice-Hall, Englewood Cliffs, NJ, USA; 1979.MATHGoogle Scholar
  16. Nikias CL, Petropulu AP: Higher-Order Spectra Analysis—A Nonlinear Signal Processing Framework. Prentice Hall, Englewood Cliffs, NJ, USA; 1993.MATHGoogle Scholar
  17. Mendel JM: Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE 1991,79(3):278-305. 10.1109/5.75086View ArticleGoogle Scholar
  18. Ribeiro MV, Marques CAG, Duque CA, Cerqueira AS, Pereira JLR: Power quality disturbances detection using HOS. IEEE Power Engineering Society General Meeting, June 2006, Monreal, QC, Canada 6.Google Scholar
  19. McDonough RN, Whalen AD: Detection of Signals in Noise. Academic Press, London, UK; 1995.Google Scholar
  20. Van Trees HL: Detection, Estimation and Modulation Theory—Parts I. Springer, New York, NY, USA; 1968.MATHGoogle Scholar
  21. Van Trees HL: Detection, Estimation and Modulation Theory—Parts III. Springer, New York, NY, USA; 1971.MATHGoogle Scholar
  22. Fishler M, Messer H: Transient signal detection using prior information in the likelihood ratio test. IEEE Transactions on Signal Processing 1993,41(6):2177-2192. 10.1109/78.218145View ArticleMATHGoogle Scholar
  23. Colonnese S, Scarano G: Transient signal detection using higher order moments. IEEE Transactions on Signal Processing 1999,47(2):515-520. 10.1109/78.740134View ArticleGoogle Scholar
  24. Giannakis GB, Tsatsanis MK: Signal detection and classification using matched filtering and higher order statistics. IEEE Transactions on Acoustics, Speech, and Signal Processing 1990,38(7):1284-1296. 10.1109/29.57557View ArticleMATHGoogle Scholar
  25. Nikias CL, Mendel JM: Signal processing with higher-order spectra. IEEE Signal Processing Magazine 1993,10(3):10-37. 10.1109/79.221324View ArticleGoogle Scholar
  26. Kauraniemi J, Laakso TI, Hartimo I, Ovaska SJ: Delta operator realizations of direct-form IIR filters. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1998,45(1):41-52. 10.1109/82.659455View ArticleGoogle Scholar
  27. Middleton RH, Goodwin GC: Improved finite word length characteristics in digital control using delta operators. IEEE Transactions on Automatic Control 1986,31(11):1015-1021. 10.1109/TAC.1986.1104162View ArticleMATHGoogle Scholar
  28. Theodoridis S, Koutroumbas K: Pattern Recognition. Academic Press, San Diego, Calif, USA; 1999.MATHGoogle Scholar
  29. Haykin S: Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 1996.MATHGoogle Scholar
  30. Jain AK, Duin RPW, Mao J: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(1):4-37. 10.1109/34.824819View ArticleGoogle Scholar

Copyright

© Moisés V. Ribeiro et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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