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7 March 2019 Optimization on metering accuracy of smart electricity meter by temperature compensation
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Proceedings Volume 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation; 1105317 (2019) https://doi.org/10.1117/12.2509352
Event: 10th International Symposium on Precision Engineering Measurements and Instrumentation (ISPEMI 2018), 2018, Kunming, China
Abstract
Smart electricity meters are playing an indispensable role in modern society, and their measurement accuracy affects the economic interests of both power units and users. In this paper, a compensating method based on neural network approximate modeling is proposed to increase the accuracy of electric energy measurement among the whole range of operational temperature. Based on the measurement data and the internal structure of the smart electricity meter, a MATLAB/Simulink model of the meter is built to evaluate the consistency of power measurement at different temperature levels. The FEM (finite element method) thermal simulation model of the meter device is carried out in ANSYS Icepak to obtain the temperature contours of the smart meter in different operating conditions. Afterwards, based on the simulation data, the component temperature in the metering circuit is evaluated according to the approximation model built by RBF (Radial basis function) neural network. At last, a temperature compensation program is realized in the MCU (Micro-Controller Unit) to adjust the metering accuracy. According to the final testing results, the proposed method significantly enhances the metering accuracy among full temperature range.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lu Wang, Guofu Zhai, Xuerong Ye, Mingdong Lv, and Songmin Yu "Optimization on metering accuracy of smart electricity meter by temperature compensation", Proc. SPIE 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation, 1105317 (7 March 2019); https://doi.org/10.1117/12.2509352
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