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This paper presents a new algorithm for fast uncertainty evaluation of root mean square (RMS) voltage measurement. It enables the evaluation of the expanded measurement uncertainty and partial uncertainties, which are useful in metrological analysis of the measurement. It can be used for any measurement system in which the RMS value is determined based on voltage samples. Various sources of uncertainty have been considered for this measurement system. The proposed algorithm is easier to implement than the commonly used uncertainty propagation method. Its operating principle is based on the Monte Carlo method. However, it allows the computation of the RMS measurement uncertainty within a significantly shorter time compared to the classical Monte Carlo method. The simulation and experimental results presented in this paper confirm the correct operation of the new algorithm and the acceleration of uncertainty computations up to 200 times in RMS measurement based on 1000 voltage samples.
Czasopismo
Rocznik
Tom
Strony
1--20
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr., wzory
Twórcy
autor
- Institute of Metrology, Electronics and Computer Science, University of Zielona Góra, Szafrana 2, 65-516 Zielona Góra, Poland
autor
- Institute of Metrology, Electronics and Computer Science, University of Zielona Góra, Szafrana 2, 65-516 Zielona Góra, Poland
autor
- Institute of Engineering and Technology, Nicolaus Copernicus University of Toruń, 87-100 Toruń, Poland
Bibliografia
- [1] Croft, F. P. (2021). American Electricians’ Handbook. 17Ed., McGraw Hill.
- [2] Belega, D., & Găşpăresc, G. (2020). Accurate Measurement of the RMS of a Sine-wave by Means of Low-cost RMS-to-Dc converters. Proceedings of the International Symposium on Electronics and Telecommunications, Timisoara, Romania, 1-4. https://doi.org/10.1109/ISETC50328.2020.9301071
- [3] Germer, H. (2001). High-precision AC measurements using the Monte Carlo method. IEEE Transactions on Instrumentation and Measurement, 50(2), 457-460. https://doi.org/10.1109/19.918165
- [4] Bulat, M., Mirković, S., Gazivoda, N., Pejić, D., Urekar, M., & Antić, B. (2024). An improved algorithm for the estimation of the root mean square value as an optimal solution for commercial measurement equipment. Microprocessors and Microsystems, 106, 1-10. https://doi.org/10.1016/j.micpro.2024.105042
- [5] Kostina, A. A., Tzvetkov, P. M., & Serov, A. N. (2020) Investigation of the method of RMS measurement based on moving averaging. Proceedings of the 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies, Niš, Serbia, 235-238. https://doi.org/10.1109/ICEST49890.2020.9232779
- [6] Krajewski, M. (2018). Constructing an uncertainty budget for voltage RMS measurement with a sampling voltmeter. Metrologia, 55(11), 95-105. https://doi.org/10.1088/1681-7575/aaa178
- [7] Novotny, M., & Sedlacek, M. (2008). RMS value measurement based on classical and modified digital signal processing algorithms. Measurement, 41(3), 236-250. https://doi.org/10.1016/j.measurement.2006.11.011
- [8] Novotny, M., & Slepicka, D. (2005). Uncertainty analysis of the phase and RMS value by non-coherent sampling in the frequency domain. Proceedings of the IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada, 2114-2117. https://doi.org/10.1109/IMTC.2005.1604547
- [9] Hegeduš, H., Mostarac, P., & Malarić, R. (2011). Comparison of RMS value measurement algorithms of non-coherent sampled signals. Measurement Science Review, 11(3), 79-84. http://www.measurement.sk/2011/Hegedus.pdf
- [10] Muciek, A. K. (2007). A method for precise RMS measurements of periodic signals by reconstruction technique with correction. IEEE Transactions on Instrumentation and Measurement, 56(2), 513-516. https://doi.org/10.1109/TIM.2007.891096
- [11] Musiał, J., Horiashchenko, K., Horiashchenko, S., & Polasik, R. (2021). Modelling of diagnostics of the technical condition of cable lines and power supply systems. Proceedings of the 20th International Conference Diagnostics of Machines and Vehicles, 351, 01008. https://doi.org/10.1051/matecconf/202135101008
- [12] D’Apice, B., Landi, C., Pelvio, A., & Rignano. N. (2007). A multi-DSP based instrument for real-time energy and PQ measurements. Metrology and Measurement Systems, 14(4) 495-506. http://www.metrology.pg.gda.pl/full/2007/M&MS_2007_495.pdf
- [13] Baccigalupi, A., Darco, M., & Liccardo, A. (2017). Parameters and methods for ADCs testing compliant with the Guide to the expression of uncertainty in measurements. IEEE Transactions on Instrumentation and Measurement, 66(3), 424-431. https://doi.org/10.1109/tim.2016.2644878
- [14] Joint Committee for Guides in Metrology (2008). Evaluation of measurement data - Guide to the expression of uncertainty in measurement (JCGM 100:2008). https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf
- [15] Joint Committee for Guides in Metrology (2008). Evaluation of measurement data. Supplement 1 to the “Guide to the expression of uncertainty in measurement” - Propagation of distributions using a Monte Carlo method (JCGM 101:2008). https://www.bipm.org/documents/20126/2071204/JCGM_101_2008_E.pdf
- [16] Harris, P. M., & Cox, M. G. (2017). On a Monte Carlo method for measurement uncertainty evaluation and its implementation. Metrologia, 51(4), 176-182. https://doi.org/10.1088/0026-1394/51/4/S176
- [17] Bullen. P. S. (2003). Handbook of Means and Their Inequalities. Springer Science+Business Media, B.V. https://doi.org/10.1007/978-94-017-0399-4
- [18] Otomański, P., & Fotowicz, P. (2011). Coverage interval as a measure of uncertainty of measurement. Proceedings of the 8th International Conference on Measurement, Smolenice, Slovakia, 3-6. https://www.measurement.sk/M2011/doc/proceedings/003_Otomanski-1.pdf
- [19] Sienkowski, S., Krajewski, M., & Lal-Jadziak, J. Implementation of the developed algorithm in Mathcad Prime computer program. https://staff.uz.zgora.pl/ssienkow/apps/soft/uncertainty_rms.zip
- [20] Cruywagen, G. C. (2023). Approximating the expectation and variance of the square root of the quadratic form in normal random variables. Social Science Research Network, Elsevier, 1-67. https://doi.org/10.2139/ssrn.4426554
- [21] Hendeby, G., & Gustafsson, F. (2007). On nonlinear transformations of Gaussian distributions. Technical Report from Automatic Control, Linkoping University, Sweden. http://users.isy.liu.se/en/rt/fredrik/reports/07SSPut.pdf
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-49e2d8bd-879b-45f8-9600-8f0f33c9f13b
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