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Intelligent Correction Algorithm for Energy Metering Data for Lean Management

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1147)

Abstract

The traditional method multiplies the synthetic error and the data reading of the energy metering system, and selects the corrected measured value. The result cannot meet the accuracy requirement of the energy metering data. To this end, an intelligent correction algorithm for energy metering data for lean management is proposed. By determining the data correction value, the intelligent correction of the data error of the energy metering system is completed, and the simulation experiment is performed on the result. The experimental results show that the data of the energy metering system is accurate after the error correction is completed by the proposed method.

Keywords

Lean management Energy metering system Error Data intelligence correction 

Notes

Acknowledgement

Project Supported by State Grid Corporation of China (Big Data Analysis Technology and Basic Platform Development in Distribution Network for Supporting Lean Management (52020116000G).

References

  1. 1.
    Song, X.L., Wang, Z.J., Li, G.Q., et al.: A high-precision method of electric power algorithm for digital watt-hour meters in smart substation. Electr. Measur. Instrum. 52(21), 60–67 (2015)Google Scholar
  2. 2.
    Shi, W.: Management method of intelligent metering based on electricity information collection system. Low Voltage Appar. (18), 72–77, 81 (2016)Google Scholar
  3. 3.
    Chen, H., Xuan, P., Wang, Y., et al.: Key technologies for integration of multitype renewable energy sources-research on multi-timeframe robust scheduling/dispatch. IEEE Trans. Smart Grid 7(1), 1 (2015)Google Scholar
  4. 4.
    Cao, M., Jiang, X., Zhao, Y.F., et al.: Design and realization of the provincial energy-metering management system in the perspective of big data analysis. J. Yunnan Minzu Univ. 26(5), 400–405 (2017)Google Scholar
  5. 5.
    Casagrande, E., Woldeamlak, S., Wei, L.W., et al.: NLP-KAOS for systems goal elicitation: smart metering system case study. IEEE Trans. Softw. Eng. 40(10), 941–956 (2014)CrossRefGoogle Scholar
  6. 6.
    Liang, W., Yang, X.X., Yao, J.H., et al.: Study on systemic error correction of build-up force standard device. Chin. J. Sci. Instrum. 34(11), 2443–2451 (2016)Google Scholar
  7. 7.
    Hu, J.Y., Zhou, Z.F., Du, X.G., Xu, Y.H.: The overall implementation of electric measurement standardization building. Power Demand Side Manage. 9(4), 5–7 (2007)Google Scholar
  8. 8.
    Li, Z.H.: Causes and improvement measures of electric energy measurement error. Appl. IC 36(6), 82–83 (2019)Google Scholar
  9. 9.
    Zhang, Y.: Standard management and application of electricity metering in the new situation. Pract. Electron. (3), 103, 105 (2016)Google Scholar
  10. 10.
    Song, L.: Analysis of effective strategies for power measurement management. Low Carbon World (15), 81, 82 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mechanical and Electrical EngineeringShandong Vocational College of Light IndustryZiboChina

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