Nonlinear modeling of measurement errors in gateway energy meters

Abstract

In order to clarify the quantitative relationship between grid parameters and measurement errors of gateway energy meters, and accurately predict the dynamic measurement errors of gateway energy meters, the author proposes a nonlinear modeling of measurement errors of gateway energy meters. Firstly, elaborate on the NARX prediction model to clarify the basic structure of the nonlinear model; Then propose the process of modeling measurement errors; Finally, through testing, identify the main power grid parameters that affect measurement errors and the optimal structure of the model. The experimental results indicate that: The comparison between the true measurement error of two electricity meters and the measurement error calculated by the nonlinear estimator shows that the Hammerstein Weiner estimator has the highest fitting degree to the true measurement error curve, with fitting degrees of 82.21 % and 85.38 % for the measurement errors of 0.2S and 0.5S electricity meters, respectively. The prediction fit of the NRAX model based on the Hammerstein Weiner nonlinear estimator reaches about 81 % under different load conditions.

Conclusion

The model determined by this method can accurately predict the dynamic measurement error of the energy meter, and the research results have positive significance for improving the efficiency of gate energy meter calibration and identifying gate energy meter faults.