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Wireless Personal Communications

, Volume 79, Issue 3, pp 2041–2058 | Cite as

Fault-Tolerant Energy Efficiency Computing Approach for Sparse Sampling Under Wireless Sensor Smart Grid

  • Bin LiEmail author
  • Bing Qi
  • Yi Sun
  • Jun Lu
  • Gangjun Gong
  • Huaguang Yan
  • Songsong Chen
Article

Abstract

Recently, energy efficiency measurement has been emphasized in China for energy conservation purpose, while there are appealing requirement for plug-and-play measuring manner without long outage time. Different with previous approaches, we present a sparse fault tolerant (SFT) method to calculate electricity energy efficiency under IEC PC-118 cloud architecture to accommodate the low speed data acquisition network. It is implemented with only one modification of the incoming line to monitoring the bus voltage. An attenuation distributed approximating approach is developed for fundamental, harmonic and interharmonic frequency and phasor estimation during energy measurement. The performance is verified with different SNR, and the results show that the proposed approach is highly resilient to noise can works well under sparse sampling environments. To guarantee the security of the power system, the trivial signal disturbance is involved as additional noise to verify the performance of SFT, and the performance is also compared with several typical algorithms, such as DFT, WIDFT, S-LMS, IDFT. The experimental results show that SFT can be used under noisy environment for SFT approach and the accuracy can be improved by the selection criteria of residues rather than improve the sampling rate. It can be applied to any form of signal and can be used online without blackout or power line interruption.

Keywords

Energy efficiency Fault-tolerant Sparse sampling  Wireless sensor network Phasor measurement Smart grid 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No 51307051), the Fundamental Research Funds for the Central Universities (12QN10, 2014ZP03) and grants from the Major National Science and Technology Special Project (2010ZX03006-005-001).

References

  1. 1.
    Sui, H., Wang, H., Ming-Shun, L., et al. (2009). An AMI system for the deregulated electricity markets. IEEE Transactions on Industry Applications, 45(6), 2104–2108.CrossRefGoogle Scholar
  2. 2.
    Zaballos, A., Vallejo, A., Majoral, M., et al. (2009). Survey and performance comparison of AMR Over PLC standards. IEEE Transactions on Power Delivery, 24(2), 604–613.CrossRefGoogle Scholar
  3. 3.
    Higgins, N., Vyatkin, V., Nair, N. K. C., et al. (2011). Distributed power system automation with IEC 61850, IEC 61499, and intelligent control. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(1), 81–92.CrossRefGoogle Scholar
  4. 4.
    IEC/TR 61334-1-4:1995. Distribution automation using distribution line carrier systems-Part 1: General considerations—Section 4: Identification of data transmission parameters concerning medium and low voltage distribution mains.Google Scholar
  5. 5.
    IEC 62056-42:2002. Electricity metering—Data exchange for meter reading, tariff and load control. Part 42: Physical layer services and procedures for connection oriented asynchronous data exchange.Google Scholar
  6. 6.
    DL/T 645-2007. Multi-function watt-hour meter communication protocol.Google Scholar
  7. 7.
    Herter, K. (2007). Residential implementation of critical-peak pricing of electricity. Energy Policy, 35(4), 2121–2130.CrossRefGoogle Scholar
  8. 8.
    Torriti, J. (2012). Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. Energy, 44(1), 576–583.CrossRefGoogle Scholar
  9. 9.
    Kopsakangas Savolainen, M., & Svento, R. (2012). Real-time pricing in the nordic power markets. Energy Economics, 34(4), 1131–1142.CrossRefGoogle Scholar
  10. 10.
    Sachdev, M., & Giray, M. (1985). A least error squares technique for determining power system frequency. IEEE Transactions on Power Application Systems, 104(2), 437–444.CrossRefGoogle Scholar
  11. 11.
    Chudamani, R., Vasudevan, K., & Ramalingam, C. S. (2009). Real-time estimation of power system frequency using nonlinear least squares. IEEE Transactions on Power Delivery, 24(3), 1021–1028.CrossRefGoogle Scholar
  12. 12.
    Gold, M. R., Ahmad, S., Browne, K., et al. (2012). Prospective comparison of discrimination algorithms to prevent inappropriate ICD therapy: Primary results of the rhythm ID going head to head trial. Heart Rhythm, 9(3), 370–377.CrossRefGoogle Scholar
  13. 13.
    Park, J.-Y., Lee, J.-K., & Cho, B.-H. (2012). Development of insulator diagnosis algorithm using least-square approximation. IEEE Transactions on Power Delivery, 27(1), 3–12.CrossRefGoogle Scholar
  14. 14.
    Wang, M., & Sun, Y. (2004). Apractical, precise method for frequency tracking and phasor estimation. IEEE Transactions on Power Delivery, 19(4), 1547–1552.CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Markham, P., Xia, T., et al. (2010). Wide-area frequency monitoring network (FNET) architecture and applications. IEEE Transactions on Smart Grid, 1(2), 159–167.CrossRefGoogle Scholar
  16. 16.
    Mai, R. K., Fu, L., Dong, Z. Y., Kirby, B., & Bo, Z. Q. (2011). An adaptive dynamic phasor estimator considering DC offset for PMU applications. IEEE Transactions on Power Delivery, 26(3), 1744–1754.CrossRefGoogle Scholar
  17. 17.
    Cho, S. H., Jang, G., & Kwon, S. H. (2010). Time-frequency analysis of power-quality disturbances via the Gabor–Wigner transform. IEEE Transactions on Power Delivery, 25(1), 494–499.CrossRefGoogle Scholar
  18. 18.
    Girgis, A., & Daniel Hwang, T. (1984). Optimal estimation of voltage phasors and frequency deviation using linear and non-linear kalman filtering: Theory and limitations. IEEE Transactions on Power Applications Systems, 103(10), 2943–2951.CrossRefGoogle Scholar
  19. 19.
    Huang, C., Lee, C., Shih, K., & Wang, Y. (2008). Frequency estimation of distorted power system signals using a robust algorithm. IEEE Transactions on Power Delivery, 23(1), 41–51.CrossRefGoogle Scholar
  20. 20.
    Leonowicz, Z., Lobos, T., & Rezmer, J. (2003). Advanced spectrum estimation methods for signal analysis in power electronics. IEEE Transactions on Industrial Electronics, 50(3), 514–519.CrossRefGoogle Scholar
  21. 21.
    Abdollahi, A., Zhang, P., Xue, H., et al. (2013). Enhanced subspace-least mean square for fast and accurate power system measurement. IEEE Transactions on Power Delivery, 28(1), 383–393.CrossRefGoogle Scholar
  22. 22.
    Xue, H., & Zhang, P. (2012). Subspace-least mean square method for accurate harmonic and interharmonic measurement in power systems. IEEE Transactions on Power Delivery, 27(3), 1260–1267.CrossRefMathSciNetGoogle Scholar
  23. 23.
    Chang, G. W., Chen, C., Liu, Y. J., & Wu, M. C. (2008). Measuring power system harmonics and interharmonics by an improved fast Fourier transform-based algorithm. Institute of Engineering Technology Generation, Transmission & Distribution, 2(2), 192–201.CrossRefGoogle Scholar
  24. 24.
    Li, B., Qi, B., Yang, J., et al. (2013). OEEABeD—Online distributed energy efficiency analysis testbed and novel monitoring approach under wireless sensor network. Journal of Internet Technology, 14(3), 467–475.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Bin Li
    • 1
    Email author
  • Bing Qi
    • 1
  • Yi Sun
    • 1
  • Jun Lu
    • 1
  • Gangjun Gong
    • 1
  • Huaguang Yan
    • 2
  • Songsong Chen
    • 2
  1. 1.School of Electric and Electronic EngineeringNorth China Electric Power UniversityBeijingPeople’s Republic of China
  2. 2.China Electric Power Research InstituteBeijingPeople’s Republic of China

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