nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

5. Frequency Estimation Methods for Smart Grid Systems

verfasst von : Engin Cemal Mengüç, Nurettin Acır

Erschienen in: Smart Grids and Their Communication Systems

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Frequency is one of the most significant parameters in the smart grid systems. Thus, accurate frequency estimation becomes an essential task for monitoring, controlling and protecting a real-time smart grid system. In this chapter, we present an overview of the frequency estimation methods in the smart grid system with a focus on real-time adaptive estimation algorithms. Primarily, in Sects. 5.1 and 5.2, the importance of the frequency estimation in the smart grid systems and the challenges encountered in its real-time applications are introduced in detail. In Sect. 5.3, a three-phase power system is then formulated as a two-phase system in the complex domain by using the well-known Clarke’s transformation so as to be able to estimate the frequency of the smart grid system in the real time. For this purpose, the adaptive real-time frequency estimation algorithms are comparatively presented as strictly and widely linear algorithms in Sect. 5.4. The strictly linear algorithms yield optimal solutions only under balanced three-phase systems, whereas the widely linear algorithms give a better solution under both balanced and unbalanced conditions due to the fact that they take into account all statistical information of the system. Considering smart grid applications in real time, the mentioned properties of these algorithms under both balanced and unbalanced conditions are proven in Sect. 5.5.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Smart Metering Systems Based on Power Line Communications

Nächstes Kapitel Demand-Side Management and Demand Response for Smart Grid

V. Choqueuse, E. Elbouchikhi, M. Benbouzid, Maximum likelihood frequency estimation in smart grid applications, in 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), 2015, pp. 1339–1344

M.R. Hossain, A.M.T. Oo, A.B.M.S. Ali, Smart grid, in Green Energy and Technology, ed. by A.B.M.S. Ali (Springer, London, 2013). https://doi.org/10.1007/978-1-4471-5210-1_2

A. Kwasinski, A. Kwasinski, Signal processing in the electrification of vehicular transportation: techniques for electric and plug-in hybrid electric vehicles on the smart grid. IEEE Signal Process. Mag. 29(5), 14–23 (2012)CrossRef

C. Nguyen, K. Srinivasan, A new technique for rapid tracking of frequency deviations based on level crossings. IEEE Trans. Power Apparatus Syst. PAS 103(8), 2230–2236 (1984)CrossRef

S.I. Jang, J.H. Choi, J.W. Kim, D.M. Choi, An adaptive relaying for the protection of a wind farm interconnected with distribution networks, in 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495), vol. 1, (2003) pp. 296–302

C.-S. Yu, A discrete Fourier transform-based adaptive mimic phasor estimator for distance relaying applications. IEEE Trans. Power Deliv. 21(4), 1836–1846 (2006)MathSciNetCrossRef

A.A. Girgis, F.M. Ham, A quantitative study of pitfalls in the FFT. IEEE Trans. Aerosp. Electron. Syst. AES-16(4), 434–439 (1980)CrossRef

G.W. Chang, C.I. Chen, Y.J. Liu, M.C. Wu, Measuring power system harmonics and interharmonics by an improved fast Fourier transform-based algorithm. Transm. Distrib. IET Gener. 2(2), 193–201 (2008)CrossRef

H. Wen, S. Guo, Z. Teng, F. Li, Y. Yang, Frequency estimation of distorted and noisy signals in power systems by FFT-based approach. IEEE Trans. Power Syst. 29(2), 765–774 (2014)CrossRef

10.

D.G. Lee, S.H. Kang, S.R. Nam, Modified dynamic phasor estimation algorithm for the transient signals of distributed generators. IEEE Trans. Smart Grid 4(1), 419–424 (2013)CrossRef

11.

E.A. Feilat, Detection of voltage envelope using Prony analysis-Hilbert transform method. IEEE Trans. Power Deliv. 21(4), 2091–2093 (2006)CrossRef

12.

C.I. Chen, G.W. Chang, R.C. Hong, H.M. Li, Extended real model of Kalman Filter for time-varying harmonics estimation. IEEE Trans. Power Deliv. 25(1), 17–26 (2010)CrossRef

13.

Z.G. Zhang, S.C. Chan, K.M. Tsui, A recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithm. IEEE Trans. Circuits Syst. II Express Briefs 55(6), 576–580 (2008)CrossRef

14.

M.D. Kusljevic, J.J. Tomic, L.D. Jovanovic, Frequency estimation of three-phase power system using weighted-least-square algorithm and adaptive FIR filtering. IEEE Trans. Instrum. Meas. 59(2), 322–329 (2010)CrossRef

15.

C. Tao, D. Shanxu, L. Bangyin, A robust parametric method for power harmonic estimation based on M-Estimators,” in 2009 IEEE 6th International Power Electronics and Motion Control Conference, 2009, pp. 2501–2506

16.

G.W. Chang, C.I. Chen, An accurate time-domain procedure for harmonics and interharmonics detection. IEEE Trans. Power Deliv. 25(3), 1787–1795 (2010)CrossRef

17.

S.K. Jain, S.N. Singh, Exact model order ESPRIT technique for harmonics and interharmonics estimation. IEEE Trans. Instrum. Meas. 61(7), 1915–1923 (2012)CrossRef

18.

J. Ren, M. Kezunovic, A hybrid method for power system frequency estimation. IEEE Trans. Power Deliv. 27(3), 1252–1259 (2012)CrossRef

19.

D. Dotta, J.H. Chow, Second harmonic filtering in phasor measurement estimation. IEEE Trans. Power Deliv. 28(2), 1240–1241 (2013)CrossRef

20.

P.J. Moore, J.H. Allmeling, A.T. Johns, Frequency relaying based on instantaneous frequency measurement. IEEE Trans. Power Deliv. 11(4), 1737–1742 (1996)CrossRef

21.

Y. Guo, M. Kezunovic, D. Chen, Simplified algorithms for removal of the effect of exponentially decaying DC-offset on the Fourier algorithm. IEEE Trans. Power Deliv. 18(3), 711–717 (2003)CrossRef

22.

V.L. Pham, K.P. Wong, Antidistortion method for wavelet transform filter banks and nonstationary power system waveform harmonic analysis. Transm. Distrib. IEE Proc. Gener. 148(2), 117–122 (2001)CrossRef

23.

J. Ren, M. Kezunovic, Real-time power system frequency and phasors estimation using recursive wavelet transform. IEEE Trans. Power Deliv. 26(3), 1392–1402 (2011)CrossRef

24.

M. Niedzwiecki, P. Kaczmarek, Tracking analysis of a generalized adaptive notch filter. IEEE Trans. Signal Process. 54(1), 304–314 (2006)CrossRef

25.

S.W. Sohn, Y.B. Lim, J.J. Yun, H. Choi, H.D. Bae, A filter bank and a self-tuning adaptive filter for the harmonic and interharmonic estimation in power signals. IEEE Trans. Instrum. Meas. 61(1), 64–73 (2012)CrossRef

26.

I. Kamwa, S.R. Samantaray, G. Joos, Wide frequency range adaptive phasor and frequency PMU algorithms. IEEE Trans. Smart Grid 5(2), 569–579 (2014)CrossRef

27.

H. Mori, K. Itou, H. Uematsu, S. Tsuzuki, An artificial neural-net based method for predicting power system voltage harmonics. IEEE Trans. Power Deliv. 7(1), 402–409 (1992)CrossRef

28.

P.K. Dash, D.P. Swain, A. Routray, A.C. Liew, Harmonic estimation in a power system using adaptive perceptrons. Transm. Distrib. IEE Proc. Gener. 143(6), 565–574 (1996)CrossRef

29.

D.O. Abdeslam, P. Wira, J. Merckle, D. Flieller, Y.A. Chapuis, A unified artificial neural network architecture for active power filters. IEEE Trans. Industr. Electron. 54(1), 61–76 (2007)CrossRef

30.

B.K. Bose, Neural network applications in power electronics and motor drives–an introduction and perspective. IEEE Trans. Industr. Electron. 54(1), 14–33 (2007)CrossRef

31.

C.I. Chen, Virtual multifunction power quality analyzer based on adaptive linear neural network. IEEE Trans. Industr. Electron. 59(8), 3321–3329 (2012)CrossRef

32.

G.W. Chang, C.I. Chen, Q.W. Liang, A two-stage ADALINE for harmonics and interharmonics measurement. IEEE Trans. Industr. Electron. 56(6), 2220–2228 (2009)CrossRef

33.

S.C. Tong, Y.M. Li, G. Feng, T.S. Li, Observer-based adaptive fuzzy backstepping dynamic surface control for a class of MIMO nonlinear systems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 41(4), 1124–1135 (2011)CrossRef

34.

S. Tong, Y. Li, P. Shi, Observer-based adaptive fuzzy backstepping output feedback control of uncertain MIMO pure-feedback nonlinear systems. IEEE Trans. Fuzzy Syst. 20(4), 771–785 (2012)CrossRef

35.

S. Tong, S. Sui, Y. Li, Fuzzy adaptive output feedback control of MIMO nonlinear systems with partial tracking errors constrained. IEEE Trans. Fuzzy Syst. 23(4), 729–742 (2015)CrossRef

36.

W. Chen, L. Jiao, R. Li, J. Li, Adaptive backstepping fuzzy control for nonlinearly parameterized systems with periodic disturbances. IEEE Trans. Fuzzy Syst. 18(4), 674–685 (2010)CrossRef

37.

T.P. Zhang, H. Wen, Q. Zhu, Adaptive fuzzy control of nonlinear systems in pure feedback form based on input-to-state stability. IEEE Trans. Fuzzy Syst. 18(1), 80–93 (2010)CrossRef

38.

S. Mitchell, K. Cohen, Fuzzy logic decision making for autonomous robotic applications, in 2014 IEEE 6th International Conference on Awareness Science and Technology (iCAST), 2014, pp. 1–6

39.

C. Laopromsukon, K. Hongesombut, J. Rungrangpitayagon, Wide-area power system control using fuzzy logic based static synchronous series compensator, in 2014 International Electrical Engineering Congress (iEECON), 2014, pp. 1–4

40.

R. Belaidi, A. Haddouche, H. Guendouz, Fuzzy logic controller based three-phase shunt active power filter for compensating harmonics and reactive power under unbalanced mains voltages. Energy Procedia 18(Supplement C), 560–570 (2012)

41.

S. Nanda, P. K. Dash, T. Chakravorti, S. Hasan, A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals. Measurement 87(Supplement C), 274–293 (2016)

42.

C.J. Ramos, A.P. Martins, A.D.S. Carvalho, Power system frequency estimation using a least mean squares differentiator. Int. J. Electr. Power Energy Syst. 87(Supplement C), 166–175 (2017)

43.

A.K. Pradhan, A. Routray, A. Basak, Power system frequency estimation using least mean square technique. IEEE Trans. Power Deliv. 20(3), 1812–1816 (2005)CrossRef

44.

A. Rastegarnia et al., Frequency Estimation of unbalanced three-phase power systems using the modified adaptive filtering. Am. J. Signal Process. 5(2A), 16–25 (2015)

45.

Y. Xia, D.P. Mandic, A widely linear least mean phase algorithm for adaptive frequency estimation of unbalanced power systems. Int. J. Electr. Power Energy Syst. 54(Supplement C), 367–375 (2014)

46.

Y. Xia, S.C. Douglas, D.P. Mandic, Adaptive frequency estimation in smart grid applications: exploiting noncircularity and widely linear adaptive estimators. IEEE Signal Process. Mag. 29(5), 44–54 (2012)CrossRef

47.

H.S. Song, K. Nam, Instantaneous phase-angle estimation algorithm under unbalanced voltage-sag conditions. IEE Proc. Gener. Transm. Distrib. 147(6), 409–415 (2000)CrossRef

48.

M. Mojiri, D. Yazdani, A. Bakhshai, Robust adaptive frequency estimation of three-phase power system. IEEE Trans. Instrum. Meas. 59(7), 1793–1802 (2010)CrossRef

49.

R. Arablouei, K. Doğançay, S. Werner, Adaptive frequency estimation of three-phase power systems. Signal Process. 109(Supplement C), 290–300 (2015)

50.

Section 1304 Smart Grid RD & Highlights, USA Energy Independence and Security Act, 2007

51.

S. Chakrabarti, E. Kyriakides, T. Bi, D. Cai, V. Terzija, Measurements get together. IEEE Power Energ. Mag. 7(1), 41–49 (2009)CrossRef

52.

V. Eckhardt, P. Hippe, G. Hosemann, Dynamic measuring of frequency and frequency oscillations in multiphase power systems. IEEE Trans. Power Deliv. 4(1), 95–102 (1989)CrossRef

53.

M.H.J. Bollen, I.Y.H. Gu, S. Santoso, M.F. McGranaghan, P.A. Crossley, M.V. Ribeiro, P.F. Ribeiro, Bridging the gap between signal and power. IEEE Signal Process. Mag. 26(4), 11–31 (2009)CrossRef

54.

D.E. Bakken, A. Bose, C.H. Hauser, D.E. Whitehead, G.C. Zweigle, Smart generation and transmission with coherent, real-time data. Proc. IEEE 99(6), 928–951 (2011)CrossRef

55.

E. Clarke, Circuit Analysis of A.C. Power Systems (Wiley, New York, 1943)

56.

M. Akke, Frequency estimation by demodulation of two complex signals. IEEE Trans. Power Deliv. 12(1), 157–163 (1997)CrossRef

57.

D.P. Mandic, S.L. Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models (Wiley, New York, 2009)CrossRef

58.

B. Picinbono, P. Chevalier, Widely linear estimation with complex data. IEEE Trans. Signal Process. 43(8), 2030–2033 (1995)CrossRef

59.

P.J. Schreier, L.L. Scharf, Second-order analysis of improper complex random vectors and process. IEEE Trans. Signal Process. 51(3), 714–725 (2003)MathSciNetCrossRef

60.

Y. Xia, S. Javidi, D.P. Mandic, A regularised normalised augmented complex least mean square algorithm, in 7th International Symposium on Wireless Communication System (ISWCS), 2010, pp. 355–359

61.

S. Javidi, M. Pedzisz, S.L. Goh, D. Mandic, The augmented complex least mean square algorithm with application to adaptive prediction problems, in Proceedings of 1st IARP Workshop Cognitive Inform. Processing, 2008, pp. 54–57

62.

C.C. Took, D.P. Mandic, Adaptive IIR filtering of noncircular complex signals. IEEE Trans. Signal Process. 57(10), 4111–4118 (2009)MathSciNetCrossRef

63.

Y. Xia, D.P. Mandic, Widely linear adaptive frequency estimation of unbalanced three-phase power systems. IEEE Trans. Instrum. Meas. 61(1), 74–83 (2012)CrossRef

64.

S.L. Goh, D.P. Mandic, An augmented CRTRL for complex valued recurrent neural networks. Neural Netw. 20(10), 1061–1066 (2007)CrossRef

65.

D.P. Mandic, S. Javidi, S.L. Goh, A. Kuh, K. Aihara, Complex valued prediction of wind profile using augmented complex statistics. Renew. Energy 34(1), 196–210 (2009)CrossRef

66.

Y. Xia, C.C. Took, D.P. Mandic, An augmented affine projection algorithm for the filtering of noncircular complex signals. Sig. Process. 90, 1788–1799 (2010)CrossRef

67.

B. Jelfs, D.P. Mandic, S.C. Douglas, An adaptive approach for the identification of improper complex signals. Sig. Process. 92, 335–344 (2012)CrossRef

68.

T. Adali, P.J. Schreier, L.L. Scharf, Complex-valued signal processing: the proper way to deal with impropriety. IEEE Trans. Signal Process. 59(11), 5101–5125 (2011)MathSciNetCrossRef

69.

P.J. Schreier, L.L. Scharf, Statistical Signal Processing of Complex—Valued Data: The Theory of Improper and Noncircular Signals (Cambridge Univ. Press, Cambridge, U.K., 2010)CrossRef

70.

B. Widrow, M.E. Hoff, Adaptive switching circuits, in Proceedings of IRE WESCON Convention Record, 1960, pp. 96–104

71.

S. Haykin, Adaptive Filter Theory, 4th edn. (Prentice Hall, Upper Saddle River, NJ, 2002)MATH

72.

B. Widrow, J. McCool, M. Ball, The complex LMS algorithm. Proc. IEEE 63(4), 719–720 (1975)CrossRef

73.

A. Tarighat, A.H. Sayed, Least mean-phase adaptive filters with application to communications systems. IEEE Signal Process. Lett. 11(2), 220–223 (2004)CrossRef

74.

T.K. Rawat, H. Parthasarathy, A continuous-time least mean-phase adaptive filter for power system frequency estimation. Int. J. Electr. Power Energy Syst. 31(2), 111–115 (2009)CrossRef

75.

S.C. Douglas, Widely-linear recursive least-squares algorithm for adaptive beamforming, in 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, pp. 2041–2044

Titel: Frequency Estimation Methods for Smart Grid Systems
verfasst von: Engin Cemal Mengüç
Nurettin Acır
Verlag: Springer Singapore
Buch: Smart Grids and Their Communication Systems
Print ISBN: 978-981-13-1767-5

Electronic ISBN: 978-981-13-1768-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-13-1768-2_5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"