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

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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.

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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).

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Authors and Affiliations

  1. School of Electric and Electronic Engineering, North China Electric Power University, Beijing, 102206, People’s Republic of China

    Bin Li, Bing Qi, Yi Sun, Jun Lu & Gangjun Gong

  2. China Electric Power Research Institute, Beijing, 100085, People’s Republic of China

    Huaguang Yan & Songsong Chen

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  1. Bin Li
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  2. Bing Qi
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Correspondence to Bin Li.

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Li, B., Qi, B., Sun, Y. et al. Fault-Tolerant Energy Efficiency Computing Approach for Sparse Sampling Under Wireless Sensor Smart Grid. Wireless Pers Commun 79, 2041–2058 (2014). https://doi.org/10.1007/s11277-014-1972-z

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