Governor tuning and digital deflector control of Pelton turbine with multiple needles for power system studies

Governor tuning and digital deflector control of Pelton turbine with multiple needles for power system studies

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(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
Your details
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.

In this study, a Pelton turbine and governor system dynamic model with deflector control was developed. The Pelton turbine established reasonable parameters for the deflector control model to effectively restrain unit frequency rise, which played the role of a similar thermal power overspeed protection. Since a Pelton turbine typically contains multiple needles, the concept of average needle opening was employed in the model. An improved orthogonal learning biogeography-based optimisation (IOLBBO) algorithm, which adopted good point set to initialise habitat, elites to maintain the tactics, and an orthogonal learning strategy to improve global search capabilities, was developed. The parameters were identified with the IOLBBO algorithm based on measured data. To develop the model in this study, various types of Pelton turbine and governor system models with different refinement degrees were analysed. Large fluctuations, like a load rejection process, verified the validity of the established deflector control model. Additionally, digital deflector control characteristics were achieved by computer programming. The non-linear Pelton turbine and water diversion system model with an elastic water hammer model that considered a non-linear relationship between the average needle opening and mechanical power developed in this study demonstrated superior simulation results in power system stability analysis.


    1. 1)
      • 1. Liscouski, B., Elliot, W.: ‘Final report on the August 14, 2003 blackout in the United States and Canada: causes and recommendations’. A report to US Department of Energy, 2004.
    2. 2)
      • 2. Pico, H.V., McCalley, J.D., Angel, A., et al: ‘Analysis of very low frequency oscillations in hydro-dominant power systems using multi-unit modeling’, IEEE Trans. Energy Convers., 2012, 27, (4), pp. 19061915.
    3. 3)
      • 3. Johnson, R.M., Chow, J.H., Dillon, M.V.: ‘Pelton turbine deflector over-speed control for a small power system’, IEEE Trans. Power Syst., 2004, 19, (2), pp. 10321037.
    4. 4)
      • 4. Patterson, S.: ‘Importance of hydro generation response resulting from the new thermal modeling and required hydro modeling improvements’. IEEE Power Engineering Society General Meeting, June 2004, pp. 17791783.
    5. 5)
      • 5. Wozniak, L., Collier, F., Foster, J.: ‘Digital simulation of an impulse turbine: the Bradley Lake project’, IEEE Trans. Energy Convers., 1991, 6, (1), pp. 3946.
    6. 6)
      • 6. Report of working group on prime mover and energy supply models for system dynamic performance studies: ‘Hydraulic turbine and turbine control models for system dynamic studies’, IEEE Trans. Power Syst., 1992, 7, (1), pp. 167179.
    7. 7)
      • 7. Yin, C.C., Muttaqi, K.M., Negnevitsky, M.: ‘Modelling of hydraulic turbine for dynamic studies and performance analysis’. Australasian Universities Power Engineering Conf., December 2007, pp. 16.
    8. 8)
      • 8. Jaeger, E.D., Janssens, N., Malfliet, B., et al: ‘Hydro turbine model for system dynamic studies’, IEEE Trans. Power Syst., 1994, 9, (4), pp. 17091715.
    9. 9)
      • 9. Ng, T.B., Walker, G.J., Sargison, J.E.: ‘Modelling of transient behaviour in a Francis turbine power plant’. Proc. of the 15th Australasian Fluid Mechanics Conf., December 2004, pp. 14.
    10. 10)
      • 10. Liu, C., Liu, N., Sun, X., et al: ‘The research and application on parameter identification of hydraulic turbine regulating system based on particle swarm optimization and uniform design’. 3rd IEEE Int. Conf. on Computer Science and Information Technology, 2010, vol. 3, pp. 605608.
    11. 11)
      • 11. Rahman, A., Saikia, L.C., Sinha, N.: ‘Load frequency control of a hydro-thermal system under deregulated environment using biogeography-based optimised three-degree-of-freedom integral-derivative controller’, IET Gener. Transm. Distrib., 2015, 9, (15), pp. 22842293.
    12. 12)
      • 12. Anagnostopoulos, J.S., Papantonis, D.E.: ‘A fast Lagrangian simulation method for flow analysis and runner design in Pelton turbines’, J. Hydrodyn., Ser. B, 2012, 24, (6), pp. 930941.
    13. 13)
      • 13. Johnson, R.M., Chow, J.H., Dillon, M.V.: ‘Pelton turbine needle control model development, validation, and governor designs’, J. Dyn. Syst. Meas. Control, 2012, 135, (1), pp. 110.
    14. 14)
      • 14. Zhang, Z., Yuan, S., Xu, Z., et al: ‘A prediction method about central heating parameters based on method of least square’. Int. Conf. on Measurement, Information and Control, August 2012, pp. 11631166.
    15. 15)
      • 15. Puma, J.Q., Colome, D.G.: ‘Parameters identification of excitation system models using genetic algorithms’, IET Gener. Transm. Distrib., 2008, 2, (3), pp. 456467.
    16. 16)
      • 16. Simon, D.: ‘A dynamic system model of biogeography-based optimization’, Appl. Soft. Comput., 2011, 11, pp. 56525661.
    17. 17)
      • 17. Liu, T., Pan, L., Meng, Q.: ‘Parameter identification of chaotic system based on quantum PSO algorithm’. 2nd Int. Conf. on Computer Science and Network Technology, 2012, vol. 41, pp. 19821985.
    18. 18)
      • 18. Xiong, G., Shi, D., Duan, X.: ‘Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning’, Comput. Oper. Res., 2014, 41, pp. 125139.

Related content

This is a required field
Please enter a valid email address