Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

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

  • Author(s): Li Wang 1 ; Jie Zhao 1 ; Dichen Liu 1 ; Jun Wang 1 ; Gang Chen 2 ; Wentao Sun 3 ; Ting Zhao 1 ; Yu Zhao 1 ; Xiaoman Qi 4
    • View affiliations
  • Source: Volume 11, Issue 13, 07 September 2017, p. 3278 – 3286
    DOI:  10.1049/iet-gtd.2016.1794 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 15/11/2016, Accepted 28/03/2017, Revised 24/02/2017, Published 04/04/2017

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.

Inspec keywords: power generation protection; power generation control; thermal power stations; search problems; digital control; hydraulic turbines; optimisation

Other keywords: power system stability analysis; unit frequency rise; nonlinear Pelton turbine; power system study; global search capability; computer programming; digital deflector control; multiple needles; governor system dynamic model; thermal power overspeed protection; water diversion system model; average needle opening; improved orthogonal learning biogeography-based optimisation algorithm; elastic water hammer model; IOLBBO algorithm; governor tuning; load rejection process; mechanical power

Subjects: Combinatorial mathematics; Power system protection; Optimisation techniques; Control of electric power systems; Combinatorial mathematics; Thermal power stations and plants; Optimisation techniques; Hydroelectric power stations and plants

References

    1. 1)
      • 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. 930–941.
    2. 2)
      • 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. 167–179.
    3. 3)
      • 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. 1779–1783.
    4. 4)
      • 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. 605–608.
    5. 5)
      • 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. 1982–1985.
    6. 6)
      • 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. 1–10.
    7. 7)
      • 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. 1032–1037.
    8. 8)
      • 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. 125–139.
    9. 9)
      • 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. 1709–1715.
    10. 10)
      • 16. Simon, D.: ‘A dynamic system model of biogeography-based optimization’, Appl. Soft. Comput., 2011, 11, pp. 5652–5661.
    11. 11)
      • 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. 1906–1915.
    12. 12)
      • 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. 39–46.
    13. 13)
      • 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. 1–4.
    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. 1163–1166.
    15. 15)
      • 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. 2284–2293.
    16. 16)
      • 15. Puma, J.Q., Colome, D.G.: ‘Parameters identification of excitation system models using genetic algorithms’, IET Gener. Transm. Distrib., 2008, 2, (3), pp. 456–467.
    17. 17)
      • 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.
    18. 18)
      • 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. 1–6.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.1794
Loading

Related content

content/journals/10.1049/iet-gtd.2016.1794
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address