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access icon free Performance limits for the amplitude estimation of power system harmonics & interharmonics

In this study, the problem of amplitude estimation of (inter)harmonics is investigated. Specifically performance limits are provided for the accuracy of unbiased amplitude estimation algorithms and for the analysis window size required by an algorithm for achieving and maintaining a desired accuracy. Cramer-Rao lower bound (CRLB) is used for the bounds on the accuracy. Unlike the existing literature concentrating on the asymptotic behaviour of the bounds, the behaviour of CRLBs for small window sizes and close frequency components is investigated. In particular, the (worst) amplitude CRLBs are shown to be inversely proportional to the squared frequency differences for small analysis window sizes and close frequency components. For the bounds on analysis window size, the concept of convergence-time is defined and illustrated. The convergence-times of unbiased amplitude estimation algorithms are then lower bounded by establishing its relationship with CRLBs. The results and ideas are illustrated using both synthetic signals and field data collected from a power grid. Numerical results show that the proposed performance limits are tight and have strong prediction capabilities for amplitude estimation algorithms having prior knowledge about the existing frequencies in the signal.

References

    1. 1)
      • 8. Uz-Logoglu, E., Salor, O., Ermis, M.: ‘Online characterization of interharmonics and harmonics of AC electric arc furnaces by multiple synchronous reference frame analysis’. IEEE Industry Applications Society Annual Meeting, Dallas, TX, 2015, pp. 111.
    2. 2)
      • 12. Ortbandt, C., Dzienis, C., Matussek, R., et al: ‘Parameter estimation in electrical power systems using Prony's method’, J. Phys., Conf. Ser., 2015, 659, p. 012013.
    3. 3)
      • 3. Jain, S.K., Singh, S.N.: ‘Harmonics estimation in emerging power system: key issues and challenges’, Electr. Power Syst. Res., 2011, 81, (9), pp. 17541766.
    4. 4)
      • 5. Duque, C.A., Silveira, P.M., Ribeiro, P.F.: ‘Visualizing time-varying harmonics using filter banks’, Electr. Power Syst. Res., 2011, 81, (4), pp. 974983.
    5. 5)
      • 11. Chen, C., Chen, Y.: ‘Comparative study of harmonic and interharmonic estimation methods for stationary and time-varying signals’, IEEE Trans. Ind. Electron., 2014, 61, (1), pp. 397404.
    6. 6)
      • 41. Stoica, P., Moses, R.L.: ‘Spectral analysis of signals’ (Pearson Prentice Hall, Upper Saddle River, NJ, USA, 2005).
    7. 7)
      • 37. Christensen, M., Jakobsson, A.: ‘Multi-pitch estimation’ (Morgan and Claypool, San Rafael, CA, USA, 2009).
    8. 8)
      • 15. Carpinelli, G., Bracale, A.: ‘An ESPRIT and DFT-based new method for the waveform distortion assessment in power systems’. The 20th Int. Conf. and Exhibition on Electricity Distribution, Prague, Czech Republic, 2009, pp. 117.
    9. 9)
      • 14. Lobos, T., Leonowicz, Z., Rezmer, J.: ‘Harmonics and interharmonics estimation using advanced signal processing methods’. 9th Int. Conf. on Harmonics and Quality of Power, Orlando, FL, 2000, vol. 1, pp. 335340.
    10. 10)
      • 7. Uz-Logoglu, E., Salor, O., Ermis, M.: ‘Real-time detection of interharmonics and harmonics of AC electric arc furnaces on GPU framework’. IEEE Industry Applications Society Annual Meeting, Cincinnati, OH, 2017, pp. 18.
    11. 11)
      • 19. Will, N.C., Cardoso, R.: ‘Implementation of the IEEE Std 1459-2010 using Kalman filter for fundamental and harmonics detection’. 3rd IEEE PES Innovative Smart Grid Technologies Europe, Berlin, Germany, 2012, pp. 17.
    12. 12)
      • 42. Van-Trees, H.L., Bell, K., Tian, Z.: ‘Detection estimation and modulation theory, part I: Detection, estimation, and filtering theory’ (John Wiley & Sons, Hoboken, NJ, USA, 2013, 2nd edn.).
    13. 13)
      • 30. Singh, S.K., Kumari, D., Sinha, N., et al: ‘Gravity search algorithm hybridized recursive least square method for power system harmonic estimation’, Eng. Sci. Technol. Int. J., 2017, 20, (3), pp. 874884.
    14. 14)
      • 21. Wang, H., Liu, S.: ‘Adaptive Kalman filter for harmonic detection in active power filter application’. IEEE Electrical Power and Energy Conf., London, ON, Canada, 2015, pp. 227232.
    15. 15)
      • 1. IEC 61000-4-7:2002(E). ‘Testing and measurement techniques – general guide on harmonics and interharmonics measurements and instrumentation, for power supply systems and equipment connected thereto’. (International Electrotechnical Commission, 2002..
    16. 16)
      • 36. Rife, D.C., Boorstyn, R.R.: ‘Multiple tone parameter estimation from discrete-time observations’, Bell Syst. Technical J., 1976, 55, (9), pp. 13891410.
    17. 17)
      • 18. Bracale, A., Carpinelli, G., Leonowicz, Z., et al: ‘Measurement of IEC groups and subgroups using advanced spectrum estimation methods’, IEEE Trans. Instrum. Meas., 2008, 57, (4), pp. 672681.
    18. 18)
      • 27. He, S., Wu, Q.H., Wen, J.Y., et al: ‘A particle swarm optimizer with passive congregation’, Biosystems, 2004, 78, (1), pp. 135147.
    19. 19)
      • 22. Yu, K.K.C., Watson, N.R., Arrillaga, J.: ‘An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking’, IEEE Trans. Power Deliv., 2005, 20, (2), pp. 15771584.
    20. 20)
      • 4. Diego, R.I., Barros, J.: ‘Global method for time-frequency analysis of harmonic distortion in power systems using the wavelet packet transform’, Electr. Power Syst. Res., 2009, 79, (8), pp. 12261239.
    21. 21)
      • 32. Singh, S.K., Sinha, N., Goswami, A.K., et al: ‘Power system harmonic estimation using biogeography hybridized recursive least square algorithm’, Int. J. Electr. Power Energy Syst., 2016, 83, pp. 219228.
    22. 22)
      • 6. Sezgin, E., Salor, O.: ‘Analysis of power system harmonic subgroups of the electric arc furnace currents based on a hybrid time-frequency analysis method’, IEEE Trans. Ind. Appl., 2019, 55, (4), pp. 43984406.
    23. 23)
      • 33. Kabalci, Y., Kockanat, S., Kabalci, E.: ‘A modified ABC algorithm approach for power system harmonic estimation problems’, Electr. Power Syst. Res., 2018, 154, pp. 160173.
    24. 24)
      • 40. Gradshteyn, I.S., Ryzhik, I.M.: ‘Table of integrals, series, and products’ (Elsevier/Academic Press, Amsterdam, Amsterdam, Netherlands, 2007, 7th edn.).
    25. 25)
      • 28. Mishra, S.: ‘A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation’, IEEE Trans. Evol. Comput., 2005, 9, (1), pp. 6173.
    26. 26)
      • 13. Yang, S., Tan, X., Wang, Y.: ‘Estimate the frequency of harmonic using the root-MUSIC algorithm’. 10th Int. Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Shanghai, China, 2017, pp. 15.
    27. 27)
      • 17. Gu, I.Y., Bollen, M.H.J.: ‘Estimating interharmonics by using sliding-window ESPRIT’, IEEE Trans. Power Deliv., 2008, 23, (1), pp. 1323.
    28. 28)
      • 31. Singh, S.K., Sinha, N., Goswami, A.K., et al: ‘Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm’, Int. J. Electr. Power Energy Syst., 2016, 80, pp. 287296.
    29. 29)
      • 20. Will, N.C., Cardoso, R.: ‘Comparative analysis between FFT and Kalman filter approaches for harmonic components detection’. 10th IEEE/IAS Int. Conf. on Industry Applications, Fortaleza, Brazil, 2012, pp. 17.
    30. 30)
      • 16. Tao, C., Shanxu, D., Bangyin, L.: ‘A robust parametric method for power harmonic estimation based on m-estimators’. IEEE 6th Int. Power Electronics and Motion Control Conf., Wuhan, China, 2009, pp. 25012506.
    31. 31)
      • 38. Liu, Q., Li, Y., Luo, L., et al: ‘Power quality management of PV power plant with transformer integrated filtering method’, IEEE Trans. Power Deliv., 2019, 34, (3), pp. 941949.
    32. 32)
      • 9. Robles, E., Pou, J., Ceballos, S., et al: ‘Frequency-adaptive stationary-reference-frame grid voltage sequence detector for distributed generation systems’, IEEE Trans. Ind. Electron., 2011, 58, (9), pp. 42754287.
    33. 33)
      • 29. Biswas, S., Chatterjee, A., Goswami, S.K.: ‘An artificial bee colony-least square algorithm for solving harmonic estimation problems’, Appl. Soft Comput., 2013, 13, (5), pp. 23432355.
    34. 34)
      • 34. Enayati, J., Moravej, Z.: ‘Real-time harmonics estimation in power systems using a novel hybrid algorithm’, IET Gen. Trans. Distrib., 2017, 11, (14), pp. 35323538.
    35. 35)
      • 23. Ray, P.K., Subudhi, B.: ‘Ensemble-Kalman-filter-based power system harmonic estimation’, IEEE Trans. Instrum. Meas., 2012, 61, (12), pp. 32163224.
    36. 36)
      • 2. Kay, S.M.: ‘Fundamentals of statistical signal processing: estimation theory’ (Prentice Hall, Upper Saddle River, NJ, USA, 1993).
    37. 37)
      • 10. Mojiri, M., Karimi-Ghartemani, M., Bakhshai, A.: ‘Processing of harmonics and interharmonics using an adaptive notch filter’, IEEE Trans. Power Deliv., 2010, 25, (2), pp. 534542.
    38. 38)
      • 25. Singh, S.K., Sinha, N., Goswami, A.K., et al: ‘Several variants of Kalman filter algorithm for power system harmonic estimation’, Int. J. Electr. Power Energy Syst., 2016, 78, pp. 793800.
    39. 39)
      • 26. Bettayeb, M., Qidwai, U.: ‘A hybrid least squares-GA-based algorithm for harmonic estimation’, IEEE Trans. Power Deliv., 2003, 18, (2), pp. 377382.
    40. 40)
      • 35. Rife, D., Boorstyn, R.: ‘Single tone parameter estimation from discrete-time observations’, IEEE Trans. Inf. Theory, 1974, 20, (5), pp. 591598.
    41. 41)
      • 39. Liu, Q., Li, Y., Hu, S., et al: ‘A transformer integrated filtering system for power quality improvement of industrial DC supply system’, IEEE Trans. Ind. Electron., 2020, 67, (5), pp. 33293339.
    42. 42)
      • 24. Subudhi, B., Ray, P.K., Mohanty, S.R., et al: ‘A comparative study on different power system frequency estimation techniques’, International Journal of Automation and Control, 2009, 3, (2/3), pp. 202215.
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