A Set of Methods for Predicting the Metrological Service-ability of Electricity Meters

The issues of improving the methods for assessing and predicting the metrological serviceability of electric energy meters, characterized by the value of the inter-verification interval with a given probability, are considered. The paper presents an analysis of the generally accepted methodology for assessing inter-verification intervals of measuring instruments according to РМГ [RMG] 74–2004 from the standpoint of correctness, efficiency, and transparency. It has been established that the model of drift of metrological characteristics of a controlled batch of measuring instruments (MI) proposed in РМГ [RMG] 74–2004, as a regression model, is an interpolation model, i.e. it determines the inter-verification interval that should be in the period between the initial and current verifications, and therefore is not predictive in fact. The necessity of developing an extrapolation model of the drift of metrological characteristics and a corresponding methodology for predicting inter-verification intervals based on methods of statistical analysis of time series is substantiated. Two possible methods for determining inter-verification intervals of electricity meters have been identified: by quantitative and alternative criteria. For each method, models for extrapolating the drift of metrological characteristics of a controlled sample of measuring instruments are proposed. To predict inter-verification intervals based on a quantitative feature, a combined drift model is justified, in which the drift of the mathematical expectation of errors of metering devices in a sample is described by a linear model, and the drift of the mean square deviation of errors is described by an exponential model. To predict inter-verification intervals based on an alternative criterion, the simultaneous consideration of two drift models is justified. The drift of the mathematical expectation of errors of metering devices in the sample is described by the model of a “linear” random process, while the standard deviation of errors is constant. The drift of the standard deviation of the errors of the measuring instruments in the sample is described by the model of a “fan” random process, while the mathematical expectation of the errors is constant. The predicted value of the inbter-verification interval is defined as the smaller value of the results of the two models. The proposed approach ensures sufficient reliability of the forecast of the inter-verification interval of electricity meters based on at least two verifycations of the controlled batch of measuring instruments (primary and first periodic).

1. On Ensuring the Uniformity of Measurements: Law of the Republic of Belarus of September 5, 1995 No. 3848-XII. National Legal Internet Portal of the Republic of Belarus. Available at: https://pravo.by/document/?guid=3871&p0=v19503848 (in Russian).

2. RMG 74–2004. Methods for Determining Inter-Verification and Inter-Calibration Intervals of Measuring Instruments. Moscow, Standartinform Publ., 2006 (in Russian).

3. MI 3676–2023. Recommendations for Determining Intervals between Verifications of Measuring Instruments. State System for Ensuring the Uniformity of Measurements. Basic Provisions. Saint Petersburg, 2023. Available at: https://39.csmrst.ru/upload/medialibrary/a43/ec1w93qqsnwak7cm76dfkyil0fyjsq2g/%D0%A0%D0%B5%D0%BA%D0%BE%D0%BC%D0%B5%D0%BD%D0%B4%D0%B0%D1%86%D0%B8%D0%B8%20%D0%9C%D0%98%203676-2023.pdf (in Russian).

4. Fridman A. E. (2008) Fundamentals of Metrology: Modern Course. Saint Petersburg, Professional Publ. 279 (in Russian).

5. Efremov L. V. (2011) Probabilistic Assessment of Metrological Reliability of Measuring Instruments: Algorithms and Programs. Saint Petersburg, Nestor-Istoriya Publ. 200 (in Russian).

6. Kalina J., Vidnerová P., Soukup L. (2023) Modern Approaches to Statistical Estimation of Measurements in the Location Model and Regression. Handbook of Metrology and Applications. Singapore, Springer Nature Singapore, 2355–2376. https://doi.org/10.1007/978-981-99-2074-7_125.

7. Venttsel E. S. (1972) Probability Theory. Moscow, Nauka Publ. 550 (in Russian).

8. Barzilovich E. Yu. (1982) Models of Maintenance of Complex Systems. Moscow, Vysshaya Shkola Publ. 306 (in Russian).

9. Sharov G. A. (2022) Statistical Metrology. Moscow, Goryachaya Liniya – Telekom Publ. 664 (in Russian).

10. Fokin Yu. A. (1985) Probabilistic-Statistical Methods in Calculations of Power Supply Systems. Moscow, Energoatomizdat Publ. 215 (in Russian).

11. Stukach O. V. (2011) Statistica Software Package in Solving Quality Management Problems.

12. Tomsk, Publishing House of Tomsk Polytechnical University. 163 (in Russian).

13. Semenikhin, K. V. (2019). Two-Sided Probability Bound for a Symmetric Unimodal Random Variable. Automation and Remote Control, 80 (3), 474–489. https://doi.org/10.1134/s000511791903007x.

14. Dobrego K. V., Koznacheev I. A. (2022) Universal Simulation Model of Battery Degradation with Optimization of Parameters by Genetic Algorithm. Energetika. Izvestiya Vysshikh Uchebnykh Zavedenii i Energeticheskikh Ob’edinenii SNG = Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations, 65 (6), 481–498. https://doi.org/10.21122/1029-7448-2022-65-6-481-498 (in Russian).

15. Farhadzadeh E. M., Muradaliyev A. Z., Abdullayeva S. A., Nazarov, A. A. (2021) Automated Analysis of Service Life of Air-Lines of the Electricity Transmission of Electric Power Systems. Energetika. Izvestiya Vysshikh Uchebnykh Zavedenii i Energeticheskikh Ob’edinenii SNG = Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations, 64 (5), 435–445. https://doi.org/10.21122/1029-7448-2021-64-5-435-445 (in Russian).