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The Error Model of the Smart Meter Under the Influence of Low Temperature

  • Xin Yin
  • Huiying Liu
  • Cong Yin
  • Jiangxue Man
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

This paper focuses on the impact of low temperature environment on the measurement performance of smart meter. Based on the actual test data of the smart meter in the low temperature environment and processing software such as Matlab and Origin, we can investigate the relationship and influence level among the low temperature environment, operating load and metering performance of the smart meter. The data from the test field of smart meter in the actual operating condition of low temperature is set up in Mohe county of Heilongjiang province. The test functions of the meter include short and long-distance detection system, load control system, protection system and environmental monitoring system. And the research results show that the low temperature environment induces a shift to negative direction of the smart meter error, so that we can obtain the mathematical model of the smart meter with the change of temperature at different loads.

Keywords

Smart meter Error Low temperature Load 

Notes

Acknowledgement

This paper is supported by Research on Performance Verification Platform Technology of Electric Energy Metering Equipment Based on Typical Environmental Conditions(Grant No. 5442JL160013).

References

  1. 1.
    Jia, M., Gu, X., Guo, Q., et al.: Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G. IEEE Wirel. Commun. 23(6), 96–106 (2017)Google Scholar
  2. 2.
    Yu, Y., Luan, W.: Smart grid and its implementations. Proc. CSEE 29(34), 1–8 (2009)Google Scholar
  3. 3.
    Jia, M., Wang, L., Yin, Z., et al.: A novel spread slotted ALOHA based on cognitive radio for satellite communications system. EURASIP J. Wireless Commun. Netw. 2016(1), 232 (2016)Google Scholar
  4. 4.
    Li, H., Xia, J.: Influence of environment temperature variation on verification result of the electricity meter and its compensation. Meas. Tech. 7, 38–39 (2001)Google Scholar
  5. 5.
    Wang, S.T., Hui, Z., Yuan, R.M., et al.: Concept and application of smart meter. Power Syst. Technol. 34(4), 17–23 (2010)Google Scholar
  6. 6.
    Cui, T., Gao, F., Ho, T., et al.: Distributed space-time coding for two-way wireless relay networks. In: IEEE International Conference on Communications, pp. 3888–3892. IEEE (2008)Google Scholar
  7. 7.
    Jing, E.B.: Smart meter system design in smart grid advanced metering infrastructure AMI. Electric. Meas. Instrum. (2010)Google Scholar
  8. 8.
    Jia, M., Xue, W., Gu, X., Guo, Q.: A simplified multiband sampling and detection method based on MWC structure for mm wave communications in 5G wireless networks. Int. J. Antennas Propag. 2015, 1–10 (2015)Google Scholar
  9. 9.
    Wang, Z., Chen, L.: Application of multivariate linear regression statistical forecast model. Stat. Decis. 5, 46–47 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Heilongjiang Electric Power Research InstituteHarbinChina
  2. 2.Harbin Research Institute of Electrical InstrumentsHarbinChina

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