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Comprehensive Quality Assessment Algorithm for Smart Meters

Author

Listed:
  • Shengyuan Liu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Fangbin Ye

    (State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China)

  • Zhenzhi Lin

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jia Yang

    (State Gird Zhejiang Ningbo Power Supply Company, Ningbo 315000, China)

  • Haigang Liu

    (State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China)

  • Yinghe Lin

    (Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310012, China)

  • Haiwei Xie

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

With the improvement of operation monitoring and data acquisition levels of smart meters, mining data associated with smart meters becomes possible. Besides, precisely assessing the operation quality of smart meters plays an important role in purchasing metering equipment and improving the economic benefits of power utilities. First, seven indexes for assessing operation quality of smart meters are defined based on the metering data and the Gaussian mixture model (GMM) clustering algorithm is applied to extract the typical index data from the massive data of smart meters. Then, the combination optimization model of index’s weight is presented with the subject experience of experts and object difference of data considered; and the comprehensive assessment algorithm based on the revised technique for order preference by similarity to an ideal solution (TOPSIS) is proposed to evaluate the operation quality of smart meters. Finally, the proposed data-driven assessment algorithm is illustrated by the actual metering data from Zhejiang Ningbo power supply company of China and practical application is briefly introduced. The results show that the proposed algorithm is effective for assessing the operation quality of smart meters and could be helpful for energy measurement and asset management.

Suggested Citation

  • Shengyuan Liu & Fangbin Ye & Zhenzhi Lin & Jia Yang & Haigang Liu & Yinghe Lin & Haiwei Xie, 2019. "Comprehensive Quality Assessment Algorithm for Smart Meters," Energies, MDPI, vol. 12(19), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3690-:d:271188
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    References listed on IDEAS

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    1. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    2. Sharma, Konark & Mohan Saini, Lalit, 2015. "Performance analysis of smart metering for smart grid: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 720-735.
    3. Ernest H. Forman & Saul I. Gass, 2001. "The Analytic Hierarchy Process---An Exposition," Operations Research, INFORMS, vol. 49(4), pages 469-486, August.
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