Research on estimating method for the smart electric energy meter's error based on parameter degradation model

The performance of the smart electric energy meter deteriorates during the operation, which will affects the accuracy of energy metering. In order to estimate the smart meter's error during the operation, a method based on parameter degradation model is proposed. The meter's degradation parameters and degradation acting parameters are determined, aiming at building parameter degradation model and putting forward error estimation constraint. Big data analysis methods are adopted in the process of solving degradation network. As the data are of multiple categories and data changing rates are variable, pre-processing method of differential normalized data is employed. Additionally, feed-forward neural network is adopted to approximate degradation characteristics, because elementary function is incapable of describing degradation network. Therefore, the smart meter's error can be estimated according to the error estimation constraint, assuming degradation acting parameters pre-determined. Case analysis results show that estimation results using the proposed method is in accordance with operating state in short time, and absolute error is less than 0.1%, demonstrating that the error of smart meters can be estimated with this method effectively and dynamically.