Optimization on metering accuracy of smart electricity meter by temperature compensation

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.