Synchronous generator third-order model parameter estimation using online experimental data

Access Full Text

Synchronous generator third-order model parameter estimation using online experimental data

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A method to estimate the dynamic parameters of the commonly used third-order dq model of a synchronous generator, based on measured electrical power, reactive power, terminal voltage, field current, field voltage and rotor angle following a small perturbation of the field voltage, is described. The parameters are estimated from two newly developed nonlinear functions for electrical power and terminal voltage by using a nonlinear least squares (NLS) algorithm. Results of simulation studies and experimental data collected from an 80 MVA, 10.5 kV generator show the efficacy of the proposed method and also reveal that the proposed method is valid for a wide range of operating conditions. For cases where rotor angle is not available, a new method for rotor angle estimation is also proposed.

Inspec keywords: least squares approximations; synchronous generators; reactive power

Other keywords: reactive power; field voltage; measured electrical power; terminal voltage; voltage 10.5 kV; nonlinear least squares algorithm; synchronous generator third-order model parameter estimation; apparent power 80 MVA; field current; online experimental data; rotor angle; nonlinear functions

Subjects: Synchronous machines; Interpolation and function approximation (numerical analysis)

References

    1. 1)
      • IEEE Standard 115-1995: ‘Test procedures for synchronous machines’. Part I: Acceptance and performance testing, Part II: Test procedures and parameter determination for dynamic analysis.
    2. 2)
      • R. Wamkeue , I. Kamwa , X. Dai-Do , A. Keyhani . Iteratively reweighted least square for maximum likelihood identification of synchronous machine parameters from on-line tests. IEEE Trans. Energy Convers. , 2 , 159 - 166
    3. 3)
      • M. Zhonggiang , Z. Chen , Z. Nan . Dynamic parameter identification of synchronous machines. Proc. Chin. Soc. Electr.l Eng. , 2 , 100 - 105
    4. 4)
      • Guoxiu, Li., Ming, F., Liang, D.: `State-space identification of synchronous machines', Proc. 1996 31st Universities Power Engineering Conf., September 1996, Iraklio, Greece, p. 114–111, Part 1.
    5. 5)
      • M. Karrari , O.P. Malik . Identification of physical parameters of a synchronous generator from on-line measurements. IEEE Trans. Energy Convers. , 2 , 407 - 415
    6. 6)
      • H.B. Karayaka , A. Keyhani , G.T. Heydt , B.L. Agrawal , D.A. Selin . Neural network based modeling of a large steam turbine-generator rotor body parameters from on-line disturbance data. IEEE Trans. Energy Convers. , 4 , 305 - 311
    7. 7)
      • T.H. Chiang , T.C. Yung , C. Chung-Linad , H. Chiung-Yi . On-line measurement-based model parameter estimation for synchronous generators: model development and identification scheme. IEEE Trans. Energy Convers. , 2 , 330 - 336
    8. 8)
      • J.J.R. Melgoza , G.T. Heydt , A. Keyhani , B.L. Agrawal , D. Selin . Synchronous machine parameter estimation using Hartley series. IEEE Trans. Energy Convers. , 1 , 49 - 54
    9. 9)
      • J.J.R. Melgoza , G.T. Heydt , A. Keyhani , B.L. Agrawal , D. Selin . An algebraic approach for identifying operating point dependent parameters of synchronous machine using orthogonal series expansions. IEEE Trans. Energy Convers. , 1 , 92 - 98
    10. 10)
      • G. Manchur , D.C. Lee , M.E. Coultes , J.D.A. Griffin , W. Watson . Generator models established by frequency response tests on a 555 MVA machine. IEEE Trans. Power Appar. Syst. , 5 , 2077 - 2084
    11. 11)
      • T.Z. Jiang , Y.X. Chen , D. Yi . The SVD applied to the identification of the basic parameters of synchronous machine. J. Changsha Univ. Electr. Power , 1 , 56 - 58
    12. 12)
      • C. Lee , T. Owen . A weighted least squares parameters estimator for synchronous generator. IEEE Trans. Power Appar. Syst. , 1 , 97 - 101
    13. 13)
      • Z. Zhao , L. Xu , J. Jiang . On-line estimation of variable parameters of synchronous machines using a novel adaptive algorithm – principles and procedures. IEEE Trans. Energy Convers. , 3 , 193 - 199
    14. 14)
      • R. Warmkeue , N.E.E. Elkadri , I. Kamwa , M. Chacha . Unbalanced transient-based finite-element modeling of large generators. J. Electr. Power Syst. Res. , 3 , 205 - 210
    15. 15)
      • M. Karrari , O.P. Malik . Nonlinear modelling of synchronous generators using wavelet transform – experimental results. J. Iran. Assoc. Electr. Electron. Eng. , 1 , 24 - 29
    16. 16)
      • P. Kundur . (1994) Power system stability and control.
    17. 17)
      • Ma, J.T., Wu, Q.H.: `Estimation of generator parameters using evolutionary programming', Int. Conf. Control'94, 1994, UK, Loughborough University of Technology, p. 1442–1447.
    18. 18)
      • M. Karrari , O.P. Malik . Identification of Heffron–Phillips model parameters for synchronous generators using on-line measurements. IEE Proc., Gener., Transm. Distrib. , 3 , 313 - 320
    19. 19)
      • Shamsoollahi, P., Malik, O.P.: `Online identification of synchronous generator using neural networks', Proc. 1996 Canadian Conf. Electrical and Computer Engineering, CCECE'96, 1996, p. 595–598, Part 2.
    20. 20)
      • M. Karrari . (2003) Power system dynamic and control.
    21. 21)
      • N. Oliver . (2000) Nonlinear system identification: from classical approach to neural network and fuzzy models.
    22. 22)
      • Z. Zhao , L. Xu , J. Jiang . On-line estimation of variable parameters of synchronous machines using a novel adaptive algorithm – estimation and experimental verification. IEEE Trans. Energy Convers. , 3 , 200 - 210
    23. 23)
      • S. Pillutla , A. Keyhani . Neural network observers for on-line tracking of synchronous generator parameters. IEEE Trans. Energy Convers. , 1 , 23 - 31
    24. 24)
      • H. Tsai , A. Keyhani , J. Demcko , R.G. Farmer . On-line synchronous machine parameter estimation from small disturbance operating data. IEEE Trans. Energy Convers. , 1 , 25 - 36
    25. 25)
      • Ghahremani, E., Karrari, M., Menhaj, M.B., Ansarimehr, P.: `Estimation of dynamic parameters of Yazd power plant synchronous generators using DC-decay test with model validation by online measurements', 98-F-ELM-692, Int. Power System Conf. PSC 2006, Tehran, Iran.
    26. 26)
      • Y.N. Yu . (1983) Electric power system dynamics.
    27. 27)
      • Karrari, M., Menhaj, M.B.: `Application of different neural networks for identification of power systems', UKACC Conf. Control 2000, 4–7 September 2000, UK, University of Cambridge.
    28. 28)
      • E. Levi , V.A. Levi . Impact of dynamic cross saturation on accuracy of saturated synchronous machine models. IEEE Trans. Energy Convers. , 2 , 224 - 230
    29. 29)
      • A.M. El-Serafi , J. Wu . Determination of the parameters representing the cross-magnetizing effect in saturated synchronous machines. IEEE Trans. Energy Convers. , 3 , 333 - 340
    30. 30)
      • Dallirrooy Fard, R., Karrari, M., Malik, O.P.: `Synchronous generator model identification using Volterra series', IEEE, PES General Meeting, 2004.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd_20080175
Loading

Related content

content/journals/10.1049/iet-gtd_20080175
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading