Synchronous generator model identification using adaptive pursuit method

Access Full Text

Synchronous generator model identification using adaptive pursuit method

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:
 
 
 
 
 
IEE Proceedings - Generation, Transmission and Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The application of an adaptive pursuit method for identification of a synchronous generator is investigated. The method is suitable for the identification of nonlinear systems in a noisy environment, particularly if online real-time adaptive control is the prime concern of the identification. The proposed method is first applied to a seventh-order nonlinear model of a synchronous generator with saturation effect and then tested on a micromachine system. In the study field voltage is considered as the input and the active output power and terminal voltages are considered as outputs of the synchronous generator. Simulation and experimental results show good accuracy of the identified models.

Inspec keywords: identification; synchronous generators; adaptive control; small electric machines; real-time systems; machine control; micromechanical devices; nonlinear systems

Other keywords: nonlinear systems; synchronous generator; noisy environment; micromachine system; identification; adaptive pursuit method; real-time adaptive control

Subjects: Small and special purpose electric machines; Synchronous machines; Self-adjusting control systems; Control of electric power systems

References

    1. 1)
      • L.A. Kilgore . Calculation of synchronous machine constants. AIEE Trans. , 1201 - 1214
    2. 2)
      • M. Burth , G.C. Verghese , M. Velez-Reyes . Subset selection for improved parameter estimation in online identification of a synchronous generator. IEEE Trans. Power Syst. , 1 , 218 - 225
    3. 3)
      • P. Kundur . (1994) Power system stability and control.
    4. 4)
    5. 5)
      • Y.N. Yu . (1983) Electric power system dynamics.
    6. 6)
      • S.H. Wright . Determination of synchronous machine constants by test. AIEE Trans. , 1331 - 1351
    7. 7)
      • M. Karrari , O.P. Malik . Identification of physical parameters of a synchronous generator from online measurements. IEEE Ttrans. Energy Convers. , 2 , 407 - 415
    8. 8)
      • Shamsollahi, P., Malik, O.P.: `Online identification of synchronous generator using neural networks', Proc. of 1996 Canadian Conf. Electrical and Computer Engineering, CCECE’96, Part 2, p. 595–598.
    9. 9)
      • J. Li , L. Ni , P. Ju , X. Chen , J. Li , L. Ni . A study on the identifiability of synchronous generator parameters. Autom. Electr. Power Syst. (China) , 3 , 9 - 12
    10. 10)
      • Karrari, M., Menhaj, M.B.: `Application of different neural networks for identification of power systems', Presented at Int. Conf. Control, 2000, U.K., p. 6.
    11. 11)
      • `Procedures for Synchronous Machines, Part I—Acceptance and Performance Testing, Part II—Test Procedures and Parameter Determination for Dynamic Analysis', , .
    12. 12)
      • 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
    13. 13)
      • K.H. Chan , E. Acha , M. Madrigal , J.A. Parle . The use of direct time-phase domain synchronous generator model in standard EMTP-type industrial packages. IEEE Power Eng. Rev. , 63 - 65
    14. 14)
    15. 15)
      • Karrari, M., Malik, O.P.: `Synchronous generator model identification using online measurements', Presented at IFAC Symp. Power Plants and Power Systems Control, 2003.
    16. 16)
      • R.H. Park . Two-reaction theory of synchronous machines. AIEE Trans.
    17. 17)
      • O. Nolles . (2001) Nonlinear system identification: from classical approaches to neural networks and fuzzy models.
    18. 18)
      • Shmilovici, , Maimon, O.: `Online identification of nonlinear systems using adaptive matching pursuit', Proc. 19th Convention of Electrical and Electronics Engineers, Nov. 1996, p. 499–502.
    19. 19)
      • Karrari, M., Malik, O.P.: `Nonlinear state space identification of a synchronous generator', Proc of IEEE Power Engineering Society General Meeting, 2003, 4, p. 2399–2404.
    20. 20)
      • `IEEE Guide for Synchronous Generator Modeling Practices and Applications in Power System Stability Analyses', , 2003, p. 1–72.
    21. 21)
    22. 22)
      • R.D. Fard , M. Karrari , O.P. Malik . Synchronous generator model identification using volterra series. IEEE Trans. Energy Convers.
    23. 23)
    24. 24)
      • Karrari, M., Malik, O.P.: `Identification of synchronous generators using discrete wavelet transform', Presented at ISAP Conf. on Intelligent System Applications to Power Systems, 2003, Lemnos, GREECE.
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-gtd_20045164
Loading

Related content

content/journals/10.1049/ip-gtd_20045164
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading