Comprehensive Quality Assessment Algorithm for Smart Meters
1
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2
State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China
3
State Gird Zhejiang Ningbo Power Supply Company, Ningbo 315000, China
4
Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310012, China
5
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Energies 2019, 12(19), 3690; https://doi.org/10.3390/en12193690
Received: 24 August 2019 / Revised: 13 September 2019 / Accepted: 23 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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.
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Keywords:
smart meters; operation quality assessment; Gaussian mixture model (GMM); combination weight optimization; revised technique for order preference by similarity to an ideal solution (TOPSIS)
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MDPI and ACS Style
Liu, S.; Ye, F.; Lin, Z.; Yang, J.; Liu, H.; Lin, Y.; Xie, H. Comprehensive Quality Assessment Algorithm for Smart Meters. Energies 2019, 12, 3690. https://doi.org/10.3390/en12193690
AMA Style
Liu S, Ye F, Lin Z, Yang J, Liu H, Lin Y, Xie H. Comprehensive Quality Assessment Algorithm for Smart Meters. Energies. 2019; 12(19):3690. https://doi.org/10.3390/en12193690
Chicago/Turabian StyleLiu, Shengyuan; Ye, Fangbin; Lin, Zhenzhi; Yang, Jia; Liu, Haigang; Lin, Yinghe; Xie, Haiwei. 2019. "Comprehensive Quality Assessment Algorithm for Smart Meters" Energies 12, no. 19: 3690. https://doi.org/10.3390/en12193690
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