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


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.


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



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).


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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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