To solve large-scale smart meter verification and periodic replacement problems, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed. In this article, a smart meter error estimation model with a loss noise filter is built. A typical loss noise filter is designed with a neural network, so that the filtered loss noise meets the Gauss–Markov condition, which paves the way for the best linear unbiased estimation (BLUE). GDRLS algorithm is applied to solve the novel estimation model, which can effectively address the problem that large loss estimation errors merge small smart meter errors. Then, a complete process of the proposed method is constructed, which can estimate both the user smart meter errors, and the loss noises accurately. Finally, the effectiveness, superiority, and applicability of the proposed method are verified through simulation analysis and practical distribution network application.

A Remote Estimation Method of Smart Meter Errors Based on Neural Network Filter and Generalized Damping Recursive Least Square

An estimation method of smart meter errors based on DREM and DRLS

To solve large-scale smart meter verification and periodic replacement problems, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed. In this article, a smart meter error estimation model with a loss noise filter is built. A typical loss noise filter is designed with a neural network, so that the filtered loss noise meets the Gauss–Markov condition, which paves the way for the best linear unbiased estimation (BLUE). GDRLS algorithm is applied to solve the novel estimation model, which can effectively address the problem that large loss estimation errors merge small smart meter errors. Then, a complete process of the proposed method is constructed, which can estimate both the user smart meter errors, and the loss noises accurately. Finally, the effectiveness, superiority, and applicability of the proposed method are verified through simulation analysis and practical distribution network application. 

Dynamic error testing method of electricity meters by a pseudo random distorted test signal

The influence of distortions in voltage and current waveforms on state-of-the-art single-phase and three-phase power meters, commonly used in the utility power system of the Slovak Republic, was studied. A computer-controlled test system capable of generating nonsinusoidal voltages and currents and a reference power quality analyzer were used for this purpose. The results of the experiments on several revenue meters are presented. Some samples exhibited high errors under the tests, while the others operated within their tolerance limit. The overview of the current standards and previously published related studies on the operation of electric energy measuring equipment under determined and random nonsinusoidal conditions is given. The discussion including possible explanations of the phenomenon supervised under the conducted tests is provided.

Contemporary electric energy meters testing under simulated nonsinusoidal field conditions

This paper formulates the dynamic error testing problem for a smart meter, with consideration and investigation of both the testing signal and the dynamic error testing method. To solve the dynamic error testing problems, the paper establishes an on-off-keying (OOK) testing dynamic current model and an OOK testing dynamic load energy (TDLE) model. Then two types of TDLE sequences and three modes of OOK testing dynamic power are proposed. In addition, a novel algorithm, which helps to solve the problem of dynamic electric energy measurement's traceability, is derived for dynamic errors. Based on the above researches, OOK TDLE sequence generation equipment is developed and a dynamic error testing system is constructed. Using the testing system, five kinds of meters were tested in the three dynamic power modes. The test results show that the dynamic error is closely related to dynamic power mode and the measurement uncertainty is 0.38%.

Zhenyu Jiang

Papers

OOK power model based dynamic error testing for smart electricity meter

Dynamic error testing method of electricity meters by a pseudo random distorted test signal

Dynamic test signal modelling and a compressed sensing based test for electric energy meter errors

The CS-OMP estimation algorithm for OFDM-based LV-PLC channels