1. bookVolume 22 (2022): Issue 4 (August 2022)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

Estimation of Energy Meter Accuracy using Remote Non-invasive Observation

Published Online: 14 May 2022
Volume & Issue: Volume 22 (2022) - Issue 4 (August 2022)
Page range: 170 - 176
Received: 27 Sep 2021
Accepted: 28 Mar 2022
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

This paper presents an error analysis of the estimation of energy meter correction factor (CF) using a remote non-invasive technique. A method of the CF estimation based on the comparison of synchronously detected power steps in power consumption profiles of meter under test and reference meter is elaborated. The dependence of meter CF estimation uncertainty upon the magnitude of power steps, the number of power steps per observation interval, and the number of meters under test monitored by one reference meter is approximated. The synthesized consumer active power profiles are used to obtain training data points that are fit by these approximating equations.

Keywords

[1] Li, N., Yang, J., Sun, Y., Wang, G., Zhang, J., Liu, C. (2018). Failure modes and effects analysis for domestic electric energy meter using in-service data. IOP Conference Series: Earth and Environmental Science (EES), 108 (5), 052036. https://doi.org/10.1088/1755-1315/108/5/05203610.1088/1755-1315/108/5/052036 Search in Google Scholar

[2] Yang, Z., Chen, Y.X., Li, Y.F., Zio, E., Kang, R. (2014). Smart electricity meter reliability prediction based on accelerated degradation testing and modeling. International Journal of Electrical Power & Energy Systems, 56, 209-219. https://doi.org/10.1016/j.ijepes.2013.11.02310.1016/j.ijepes.2013.11.023 Search in Google Scholar

[3] Nakutis, Ž., Kaškonas, P., Saunoris, M., Daunoras, V., Jurčevic, M. (2021). A framework for remote in-service metrological surveillance of energy meters. Measurement, 168, 108438. https://doi.org/10.1016/j.measurement.2020.10843810.1016/j.measurement.2020.108438 Search in Google Scholar

[4] Nakutis, Ž., Kaškonas, P. (2020). A contemplation on electricity meters in-service surveillance assisted by remote error monitoring. Energies, 13 (20), 5245. https://doi.org/10.3390/en1320524510.3390/en13205245 Search in Google Scholar

[5] European Commission. (2018). Smart grids and meters. https://ec.europa.eu/energy/topics/markets-and-consumers/smart-grids-and-meters/overview_en?redir=1 Search in Google Scholar

[6] Nakutis, Ž., Saunoris, M., Ramanauskas, R., Daunoras, V., Lukočius, R., Marčiulionis, P. (2019). A method for remote estimation of wattmeter’s adjustment gain. IEEE Transactions on Instrumentation and Measurement, 68 (3), 713-721. https://doi.org/10.1109/TIM.2018.285711810.1109/TIM.2018.2857118 Search in Google Scholar

[7] Lukočius, R., Nakutis, Ž., Daunoras, V., Deltuva, R., Kuzas, P., Račkiene, R. (2019). An analysis of the systematic error of a remote method for a wattmeter adjustment gain estimation in smart grids. Energies, 12 (1), 37. https://doi.org/10.3390/en1201003710.3390/en12010037 Search in Google Scholar

[8] Seppa, H. (2007). Method and system for the calibration of meters. WO 2007/063180 A1, Patent Cooperation Treaty (PCT). http://www.freepatentsonline.com/WO2007063180.html Search in Google Scholar

[9] Kong, X., Ma, Y., Zhao, X., Li, Y., Teng, Y. (2019). A recursive least squares method with double-parameter for online estimation of electric meter errors. Energies, 12 (5), 805. https://doi.org/10.3390/en1205080510.3390/en12050805 Search in Google Scholar

[10] Liu, F., Liang, C., He, Q. (2020). Remote malfunctional smart meter detection in edge computing environment. IEEE Access, 8, 67436-67443. https://doi.org/10.1109/ACCESS.2020.298572510.1109/ACCESS.2020.2985725 Search in Google Scholar

[11] Nakutis, Ž., Rinaldi, S., Kuzas, P., Lukočius, R. (2020). A method for noninvasive remote monitoring of energy meter error using power consumption profile. IEEE Transactions on Instrumentation and Measurement, 69 (9), 6677-6685. https://doi.org/10.1109/TIM.2020.300240210.1109/TIM.2020.3002402 Search in Google Scholar

[12] Daunoras, V. (2021). A method for remote monitoring of electrical energy meter errors. Thesis, Kaunas University of technology, Kaunas, Lithuania. https://en.ktu.edu/events/v-daunoras-a-method-for-remote-monitoring-of-electrical-energy-meter-errors-doctoral-dissertation-defence/ Search in Google Scholar

[13] Abate, F., Carratu, M., Liguori, C., Paciello, V. (2019). A low cost smart power meter for IoT. Measurement, 136, 59-66. https://doi.org/10.1016/j.measurement.2018.12.06910.1016/j.measurement.2018.12.069 Search in Google Scholar

[14] Artale, G., Cataliotti, A., Cosentino, V., Di Cara, D., Fiorelli, R., Guaiana, S., Panzavecchia, N., Tine, G. (2018). A new PLC-based smart metering architecture for medium/low voltage grids: Feasibility and experimental characterization. Measurement, 129, 479-488. https://doi.org/10.1016/j.measurement.2018.07.07010.1016/j.measurement.2018.07.070 Search in Google Scholar

[15] Avancini, D.B., Rodrigues, J.J.P.C., Martins, S.G.B., Rabelo, R.A.L., Al-Muhtadi, J., Solic, P. (2019). Energy meters evolution in smart grids: A review. Journal of Cleaner Production, 217, 702-715. https://doi.org/10.1016/j.jclepro.2019.01.22910.1016/j.jclepro.2019.01.229 Search in Google Scholar

[16] Matanza, J., Alexandres S., Rodríguez-Morcillo, C. (2014). Advanced metering infrastructure performance using European low-voltage power line communication networks. IET Communications, 8 (7), 1041-1047. https://doi.org/10.1049/iet-com.2013.079310.1049/iet-com.2013.0793 Search in Google Scholar

[17] Bat-Erdene, B., Lee, B., Kim, M.-Y., Ahn, T.H., Kim, D. (2013). Extended smart meters-based remote detection method for illegal electricity usage. IET Generation, Transmission & Distribution, 7 (11), 1332-1343. https://doi.org/10.1049/iet-gtd.2012.028710.1049/iet-gtd.2012.0287 Search in Google Scholar

[18] Pflugradt, N. (2021). Load profile generator. https://www.loadprofilegenerator.de Search in Google Scholar

[19] Pflugradt, N., Teuscher, J., Platzer, B., Schufft, W. (2013). Analysing low-voltage grids using a behaviour based load profile generator. In Renewable Energy & Power Quality Journal, 1 (11), 361-365. https://doi.org/10.24084/repqj11.30810.24084/repqj11.308 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo