Refinement of the mathematical model of electrical energy measurement uncertainty in reduced load mode
DOI:
https://doi.org/10.15587/1729-4061.2022.262260Keywords:
metering unit, electricity meter, measurement uncertainty, fuzzy function, current transformerAbstract
The object of the study is a three-phase commercial electricity metering unit for 380 V electrical grids. The uncertainty of electricity measurement in the reduced load mode is estimated by the relative deviation of the active energy, measured by the metering unit, from the actual value. The specified deviation is considered as the value of relative deviations on measuring channels, weighted by phase currents. The method of estimating the uncertainty of electricity measurement by one channel of the metering unit is based on the approach to estimating non-random uncertainty using the fuzzy set theory. The parameters of membership functions for the relative deviation of the metering unit readings are estimated at fixed levels of the channel current. Approximation of such functions for different current levels allows you to obtain a set of boundaries of the L-R type fuzzy function corresponding to a set of confidence levels. This allows determining the impact of the load phase current on the measurement uncertainty if the amount of empirical data is limited. The mathematical model for estimating the uncertainty of electricity measurement at reduced load using a fuzzy function was refined. The proposed model differs from the known ones by taking into account the influence of load values for each phase of the metering unit on the measurement uncertainty indicators. The method for determining the membership function and the marginal confidence level, which characterize the uncertainty of energy metering by the metering unit, is proposed. The mathematical modeling results are confirmed as adequate to the experimental data. The proposed model for estimating the measurement uncertainty allows estimating the level of underestimation and clarifying financial calculations between the seller and the buyer of electricity.
References
- Chen, X., Song, C., Wang, T. (2021). Analysis of electricity loss and electricity consumption law in low-voltage areas: a case study. Journal of Physics: Conference Series, 2022 (1), 012016. doi: https://doi.org/10.1088/1742-6596/2022/1/012016
- Carr, D., Thomson, M. (2022). Non-technical electricity losses. Energies, 15 (6), 2218. doi: https://doi.org/10.3390/en15062218
- IEC 61869-2:2012. Instrument transformers - Part 2: Additional requirements for current transformers. Available at: https://webstore.iec.ch/publication/6050
- Kato, H., Imai, H. (2012). Uncertainty evaluation for the composite error of energy meter and instrument transformer. XX IMEKO World Congress Metrology for Green Growth. Busan. Available at: https://www.imeko.org/publications/wc-2012/IMEKO-WC-2012-TC4-P3.pdf
- Demerdziev, K., Dimchev, V. (2021) Analysis of errors in active power and energy measurements under random harmonic distortion conditions. Measurement Science Review, 21 (6), 168–179. doi: https://doi.org/10.2478/msr-2021-0023
- Skorkowski, A., Kampik, M., Musioł, K., Nocon, A. (2022). The errors of electronic energy meters that measure energy consumed by LED lighting. Energies, 15 (9), 3254. doi: https://doi.org/10.3390/en15093254
- EA-4/02 M: 2022. Evaluation of the uncertainty of measurement in calibration. European co-operation for Accreditation. Available at: https://european-accreditation.org/wp-content/uploads/2018/10/EA-4-02.pdf
- Ferrero, A., Salicone, S. (2018). A comparison between the probabilistic and possibilistic approaches: the importance of a correct metrological information. IEEE Transactions on Instrumentation and Measurement, 67 (3), 607–620. doi: https://doi.org/10.1109/tim.2017.2779346
- SO/IEC Guide 98-1:2009(en). Uncertainty of measurement – Part 1: Introduction to the expression of uncertainty in measurement. Available at: https://www.iso.org/obp/ui/#iso:std:iso-iec:guide:98:-1:ed-1:v1:en
- Mróz, P., Olencki, A., Bukowiec, A. (2014). A Method of Determining an Electric Energy Meter Maximum Uncertainty. Lecture Notes in Electrical Engineering, 405–410. doi: https://doi.org/10.1007/978-3-642-54900-7_57
- Ferrero, A., Prioli, M., Salicone, S. (2013). The evaluation of uncertainty contributions due to uncompensated systematic effects. 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). doi: https://doi.org/10.1109/i2mtc.2013.6555571
- Willink, R. (2013) An improved procedure for combining type A and type B components of measurement uncertainty. International Journal of Metrology and Quality Engineering, 4 (1), 55–62. doi: https://doi.org/10.1051/ijmqe/2012038
- Salicone, S., Prioli, M. (2018). Measuring uncertainty within the theory of evidence. Springer Nature Switzerland AG, 330. doi: https://doi.org/10.1007/978-3-319-74139-0
- BS EN 50160:2010+A3:2019. Voltage characteristics of electricity supplied by public electricity networks. Available at: https://www.en-standard.eu/bs-en-50160-2010-a3-2019-voltage-characteristics-of-electricity-supplied-by-public-electricity-networks/
- Xia, X., Wang, Z., Gao, Y. (2000). Estimation of non-statistical uncertainty using fuzzy-set theory. Measurement Science and Technology, 11 (4), 430–435. doi: https://doi.org/10.1088/0957-0233/11/4/314
- Wang, C., Huang, Y., Shao, M., Chen, D. (2018). Uncertainty measures for general fuzzy relations. Fuzzy Sets and Systems, 360, 82–96. doi: https://doi.org/10.1016/j.fss.2018.07.006
- Marmolejo-Ramos, F., Tian, S. (2010). The shifting boxplot. A boxplot based on essential summary statistics around the mean. International Journal of Psychological Research, 3 (1), 37–46. doi: https://doi.org/10.21500/20112084.823
- GPL Reference Guide for IBM SPSS Statistics (2021). IBM Corporation. Available at: https://www.ibm.com/docs/en/SSLVMB_28.0.0/pdf/GPL_Reference_Guide_for_IBM_SPSS_Statistics.pdf
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Kateryna Vasylets, Volodymyr Kvasnikov, Sviatoslav Vasylets
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.