Research on the Integral Metrological Performance of Evaluation Method of Smart Electricity Meters

Huanning, Wang; Huang, Yan; Hu, Yao; Ding, Xiang; Tan, Hengyu; Jin, Senlin

doi:10.1109/icsgsc.2019.00-17

Cited by 1 publication

(1 citation statement)

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“…Literature [6] tested the voltage, current and circuit impedance of clock battery of smart watt-hour meter under different working conditions and different temperatures, but only carried out low temperature test at 0℃ and below. Literature [13] and literature [14] established a mathematical model based on linear regression method to analyze the relationship between pulse voltage and the remaining energy of Li/SOCl2 battery, but it is difficult to carry out pulse voltage test on its built-in battery in practical application of watthour meters. In literature [15], the relationship between storage temperature, storage time and battery remaining capacity was measured experimentally, and piecewise polynomial models at different temperatures were obtained based on the least square fitting method.…”

Section: Introductionmentioning

confidence: 99%

Influence of operating temperature on self-discharge of built-in battery of smart electric energy meter

Zheng,

Yang,

Cui

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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The built-in battery of smart electric energy meter is used to realize the functions of information storage, special event recording, clock normal operation and so on. It is the key component of the electric energy meter. However, in the field operation, a high proportion of smart electric energy meters occurred battery under-voltage warning, and these meters are in the service life required by the technical specifications. Research on this problem is carried out. The purpose is to find out the cause of battery under-voltage fault, and put forward improvement measures to improve the service life of smart electric energy meter. The hourly temperature in the measuring box at different installation positions is measured. It indicates that the electric energy meter in operation can be in high temperature for a long time. The capacity prediction model of the built-in battery of smart electric energy meter based on Arrhheninus formula and BP neural network was established. It was found that the self-discharge rate of the Li/SOCl2 battery would increase exponentially when the temperature increased.

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Section: Introductionmentioning

confidence: 99%