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Abstract

In this paper it is shown that M class PMU (Phasor Measurement Unit) reference model for phasor estimation recommended by the IEEE Standard C37.118.1 with the Amendment 1 is not compliant with the Standard. The reference filter preserves only the limits for TVE (total vector error), and exceeds FE (frequency error) and RFE (rate of frequency error) limits. As a remedy we propose new filters for phasor estimation for M class PMU that are fully compliant with the Standard requirements. The proposed filters are designed: 1) by the window method; 2) as flat-top windows; or as 3) optimal min-max filters. The results for all Standard compliance tests are presented, confirming good performance of the proposed filters. The proposed filters are fixed at the nominal frequency, i.e. frequency tracking and adaptive filter tuning are not required, therefore they are well suited for application in lowcost popular PMUs.
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Authors and Affiliations

Krzysztof Duda
Tomasz P. Zieliński
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Abstract

A new solution to the problem of frequency estimation of a single sinusoid embedded in the white Gaussian noise is presented. It exploits, approximately, only one signal cycle, and is based on the well-known 2nd order autoregressive difference equation into which a downsampling is introduced. The proposed method is a generalization of the linear prediction based Prony method for the case of a single undamped sinusoid. It is shown that, thanks to the proposed downsampling in the linear prediction signal model, the overall variance of the least squares solution of frequency estimation is decreased, when compared to the Prony method, and locally it is even close to the Cramér–Rao Lower Bound, which is a significant improvement. The frequency estimation variance of the proposed solution is comparable with, computationally more complex, the Matrix Pencil and the Steiglitz–McBride methods. It is shown that application of the proposed downsampling to the popular smart DFT frequency estimation method also significantly reduces the method variance and makes it even better than the least squares smart DFT. The noise immunity of the proposed solution is achieved simultaneously with the reduction of computational complexity at the cost of narrowing the range of measured frequencies, i.e. a sinusoidal signal must be sufficiently oversampled to apply the proposed downsampling in the autoregressive model. The case of 64 samples per period with downsampling up to 16, i.e. 1/4th of the cycle, is presented in detail, but other sampling scenarios, from 16 to 512 samples per period, are considered as well.
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Bibliography

[1] Kay, S. M. (1993). Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall
[2] Kay, S. M., & Marple, S. L. (1981). Spectrum analysis – A modern perspective. Proc. IEEE, 69, 1380–1419. https://doi.org/10.1109/PROC.1981.12184
[3] Kay, S. M. (1987). Modern Spectrum Analysis. Prentice-Hall.
[4] Zielinski, T. P., & Duda, K. (2011). Frequency and damping estimation methods - an overview. Metrology and Measurement Systems, 18(3), 505–528. https://doi.org/10.2478/v10178-011-0051-y
[5] Duda, K., & Zielinski, T. P. (2013). Efficacy of the frequency and damping estimation of a real-value sinusoid. IEEE Instrumentation & Measurement Magazine, 16(1), 48–58. https://doi.org/10.1109/ MIM.2013.6495682
[6] Borkowski, J., Kania, D., & Mroczka, J. (2018). Comparison of sine-wave frequency estimation methods in respect of speed and accuracy for a few observed cycles distorted by noise and harmonics. Metrology and Measurement Systems, 25(1), 283–302. https://doi.org/10.24425/119567
[7] Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 66(1), 51–83. https://doi.org/10.1109/PROC.1978.10837
[8] Zygarlicki, J., Zygarlicka, M., Mroczka, J., & Latawiec, K. J. (2010). A reduced Prony’s method in power-quality analysis – parameters selection. IEEE Transactions on Power Delivery, 25(1), 979–986. https://doi.org/10.1109/TPWRD.2009.2034745
[9] Zygarlicki, J., & Mroczka, J. (2014). Prony’s method with reduced sampling – numerical aspects. Metrology and Measurement Systems, 21(2), 521–534. https://doi.org/10.2478/mms-2014-0044
[10] Zygarlicki, J. (2017). Fast second order original Prony’s method for embedded measuring systems. Metrology and Measurement Systems, 24(3), 721–728. https://doi.org/10.1515/mms-2017-0058
[11] Hua, Y., & Sarkar, T. K., (1990). Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoid in noise. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(4), 814–824. https://doi.org/10.1109/29.56027
[12] Steiglitz, K.,&McBride, L. (1965). A technique for identification of linear systems. IEEE Transactions on Automatic Control, 10(3), 461–464. https://doi.org/10.1109/TAC.1965.1098181
[13] McClellan, J. H., & Lee, D. (1991). Exact equivalence of the Steiglitz–McBride iteration and IQML. IEEE Transactions on Signal Processing, 39(1), 509–512. https://doi.org/10.1109/78.80841
[14] Wu, R. C., & Chiang, C. T. (2010). Analysis of the exponential signal by the interpolated DFT algorithm. IEEE Transactions on Instrumentation and Measurement, 59(12), 3306–3317. https://doi.org/10.1109/TIM.2010.2047301
[15] Derviškadic, A., Romano, & P., Paolone, M. (2018). Iterative-Interpolated DFT for Synchrophasor Estimation: A Single Algorithm for P- and M-Class Compliant PMUs. IEEE Transactions on Instrumentation and Measurement, 67(2), 547–558. https://doi.org/10.1109/TIM.2017.2779378
[16] Jacobsen, E., & Kootsookos, P. (2007). Fast, accurate frequency estimators. IEEE Signal Processing Magazine, 24(2), 123–125. https://doi.org/10.1109/MSP.2007.361611
[17] Duda, K., & Barczentewicz, S. (2014). Interpolated DFT for sin α (x) windows. IEEE Transactions on Instrumentation and Measurement, 63(3), 754–760. https://doi.org/10.1109/TIM.2013.2285795
[18] Yang, J. Z., & Liu, C. W. (2000). A precise calculation of power system frequency and phasor. IEEE Transactions on Power Delivery, 15(1), 494–499. https://doi.org/10.1109/61.852974
[19] Yang, J. Z., & Liu, C. W. (2001). A precise calculation of power system frequency. IEEE Transactions on Power Delivery, 16(2), 361–366. https://doi.org/10.1109/61.924811
[20] Xia, Y., He, Y., Wang, K., Pei, W., Blazic, Z., & Mandic, D. P. (2017). A complex least squares enhanced smart DFT technique for power system frequency estimation. IEEE Transactions on Power Delivery, 32(2), 1270–1278. https://doi.org/10.1109/TPWRD.2015.2418778
[21] Li, Z. (2021). A total least squares enhanced smart DFT technique for frequency estimation of unbalanced three-phase power systems. International Journal of Electrical Power & Energy Systems, 128, 106722. https://doi.org/10.1016/j.ijepes.2020.106722
[22] Xu, S., Liu, H., & Bi, T. (2020). A novel frequency estimation method based on complex Bandpass filters for P-class PMUs with short reporting latency. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2020.3038703
[23] Duda, K., & Zielinski, T. P. (2021). P Class and M Class Compliant PMU Based on Discrete- Time Frequency-Gain Transducer. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2021.3076831
[24] IEC, IEEE. (2018). Measuring relays and protection equipment – Part 118–1: Synchrophasor for power systems – Measurements (IEC/IEEE Standard No. 60255-118-1).
[25] Moon, T. K., & Stirling W. C. (1999). Mathematical Methods and Algorithms for Signal Processing. Prentice Hall.

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Authors and Affiliations

Krzysztof Duda
1
Tomasz P. Zieliński
2

  1. AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Measurement and Electronics, al. Mickiewicza 30, 30-059 Kraków, Poland
  2. AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Institute of Telecommunications, al. Mickiewicza 30, 30-059 Kraków, Poland

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