In the paper, a non-invasive remote monitoring method of error of electricity energy meter is introduced and its achievable precision is investigated. The aim of the method is to determine remotely the smart energy meter active power relative measurement error using the reference energy meter connected at the input of a low voltage distribution grid segment. The meter error estimation is carried out despite other customers’ electrical load changes observable by the reference meter and power fluctuations in the distribution network. The method is based on the real time analysis of natural customer power consumption profiles without any additional load injection for error monitoring needs. A power step in the customer power consumption profile is detected by the meter under test and then the step magnitude is delivered for the comparison with the power step magnitude estimated synchronously by the reference meter. The analysis of the method error is conducted by applying simulation of the benchmark distribution grid which includes 26 energy meters and utilizing the open data power consumption profiles. It is shown that after two to three days of averaging (including approximately 170 power steps) of the meter error estimates it becomes possible to achieve an uncertainty of meter error estimation approximately 0.63 %. The feasibility of the method implementation scenarios with the state-of-the-art energy meter data collection protocols in advanced measurement infrastructure by means of occupancy index modeling is performed.

A method for remote wattmeter (RW) adjustment is introduced in this paper. The estimated adjustment gain is intended for corrections of wattmeter's indications. Currently, legal metrology regulations do not allow such type of adjustment of certified instruments in the field. However, the proposed adjustment could be applicable in the areas of private or industrial subaccounting, home automation, and so on. If the achievable uncertainty could be specified, the method might be suggested for the wattmeter's calibration or verification. The implementation of remote adjustment becomes possible if both remote and reference wattmeters are connected to the same electrical grid and are equipped with a data communication channel. The main idea of the method is based on a temporary injection of the additional load at the location of remote device and acquisition of synchronized power readings by both reference and remote devices. The acquired readings are then processed to derive an adjustment gain of the RW. The proposed method aims to cancel an influence of the measurement unit propagating medium between the reference and remote devices. It is assumed that the characteristics of the medium are constant during the short period that is necessary to acquire readings. It is demonstrated that using the simulation of the proposed setup in the case of typical power losses in distribution lines, the adjustment gain can be estimated precisely. It is shown that in the case of remote measurement, the injected load power uncertainty can be reduced by averaging the acquired power samples.

The increasing number of zero-emission vehicles on the roads demands novel vehicle charging solutions that ensure convenience, safety, increased charging infrastructure availability, and aesthetics. Wireless charging technology is seen as the one that could assure these desirable properties and could be applied not just in conventional implementations but also in off-grid solutions together with roadway energy harvesting systems. Both approaches require proper transfer of energy metering methods. In this paper, a method for measuring the power transferred to the load in a wireless charging system is presented, and its systematic error is assessed in the relevant range of influencing factors. The novelty of the method is that it does not require any metrologically certified measurement instrumentation on the receiver side of the wireless charging system. The error analysis is performed using a numerical simulation. Considered error-influencing factors included secondary side electrical load, coils’ coupling coefficient and quality factor, current and voltage quantization resolution, and compensation topology type (serial-serial (SS) and serial-parallel (SP)). It was determined that the systematic error of the power assessment does not exceed 0.7% for SS and 1.1% for SP topologies when the coupling coefficient is in the range of 0.05 to 0.4 and the quality factor of the resonant system is in the range of 100 to 800.

Smart energy meters supporting bidirectional data communication enable novel remote error monitoring applications. This research targets characterization of the systematic worst-case error of the previously published remote watthour meter’s gain estimation method based on the comparison of synchronous measurements by the reference and meter under test. To achieve the research aim a methodology based on global maximization of the systematic error objective function assuming the typical low voltage electrical distribution network operation parameters ranges as defined by the standard recommendations for network design. To cross verify the reliability of the assessed solutions the suggested error analysis methodology was implemented utilizing two stochastic global extremum search techniques (genetic algorithms, pattern search) and the third one utilizing nonlinear programming solver. It was determined that the wattmeter adjustment gain worst-case error does not exceed 0.5% if the remote wattmeter monitored load power factor is larger than 0.1 and a network is designed according to the recommendation of the acceptable voltage drop less than 5%. For a load exhibiting power factor larger than $\cos \phi =$ 0.9 the worst-case error was found to be less than 0.1%. It is concluded therefore that considering the systematic worst-case error the previously suggested remote wattmeter adjustment gain estimation method is suitable for remote error monitoring of Class 2 and Class 1 wattmeters.