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Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

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Traditionally, hydro turbine governor applications mainly rely on classical proportional–integral–derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

References

    1. 1)
      • 11. Maciejowski, J.M.: ‘Predictive control: with constraints’ (Prentice-Hall, Englewood Cliffs, NJ, 2002).
    2. 2)
      • 36. Rossiter, J.A., Neal, P.W., Yao, L.: ‘Applying predictive control to a fossil-fired power station’, Trans. Inst. Meas. Control, 2002, 24, (3), pp. 177194.
    3. 3)
      • 25. Dragicevic, T.: ‘Model predictive control of power converters for robust and fast operation of AC microgrids’, IEEE Trans. Power Electron., 2018, 33, (7), pp. 63046317.
    4. 4)
      • 44. Ljung, L.: ‘MATLAB & SIMULINK:System Identification Toolbox - User's Guide’. MathWorks, 2018.
    5. 5)
      • 26. Zhang, Y., Liu, J., Yang, H.: ‘New insights into model predictive control for three-phase power converters’, IEEE Trans. Ind. Appl., 2019, 55, (2), pp. 19731982.
    6. 6)
      • 7. Gonggui, C., Yangwei, D., Yanyan, G., et al: ‘PID parameters optimization research for hydro turbine governor by an improved fuzzy particle swarm optimization algorithm’, The Open Electr. Electron. Eng. J., 2016, 10, pp. 101117.
    7. 7)
      • 31. Henriksen, L.C., Hansen, M.H., Poulsen, N.K.: ‘Wind turbine control with constraint handling: a model predictive control approach’, IET Control Theory and Applications, 2012, 6, (11), pp. 17221734.
    8. 8)
      • 17. Liu, X., Jiang, D., Lee, K.Y.: ‘Decentralized fuzzy MPC on spatial power control of a large PHWR’, IEEE Trans. Nucl. Sci., 2016, 63, (4), pp. 23432351.
    9. 9)
      • 15. Beus, M., Pandzic, H.: ‘Application of model predictive control algorithm on a hydro turbine governor control’. 2018 Power Systems Computation Conf. (PSCC), Dublin, Ireland, 2018.
    10. 10)
      • 14. Munoz Hernandez, G.A., Jones, D.I.: ‘MIMO generalized predictive control for a hydroelectric power station’, IEEE Trans. Energy Convers., 2006, 21, (4), pp. 921929.
    11. 11)
      • 13. Jones, D., Mansoor, S.: ‘Predictive feedforward control for a hydroelectric plant’, IEEE Transactions on Control System Technology, 2004, 12, (6), pp. 921929.
    12. 12)
      • 9. Xiao, Z., Meng, S., Malik, O.P.: ‘One-step-ahead predictive control for hydro turbine governor’, Math. Probl. Eng., 2015, 2015, pp. 110.
    13. 13)
      • 27. Tomlinson, M., Mouton, H.T., Kennel, R., et al: ‘A fixed switching frequency scheme for finite-control-Set model predictive control - concept and algorithm’, IEEE Trans. Ind. Electron., 2016, 63, (12), pp. 76627670.
    14. 14)
      • 28. Hu, J., Zhu, J., Dorrel, D.G.: ‘Model predictive control of inverters for both islanded and grid-connected operations in renewable power generations’, IET Renew. Power Gener., 2014, 8, (3), pp. 240248.
    15. 15)
      • 24. Hug-Glanzmann, G.: ‘Predictive control for balancing wind generation variability using run-of-river power plants’. IEEE Power and Energy Society General Meeting, Detroit, USA, 2011, pp. 18.
    16. 16)
      • 33. Morsi, A., Abbas, S.H., Mohamed, A.M.: ‘Wind turbine control based on a modified model predictive control scheme for linear parameter-varying systems’, IET Control Theory Appl., 2017, 11, (17), pp. 30563068.
    17. 17)
      • 19. Krishnan, A., Patil, B.V., Gooi, H.B., et al: ‘Predictive control based framework for optimal scheduling of combined cycle gas turbines’. Proc. of the 2016 American Control Conf. (ACC), Boston, USA, 2016.
    18. 18)
      • 1. Culberg, J., Negnevitsky, M., Kashem, K.A.: ‘Hydro turbine governor control: theory, techniques and limitations’. Australasian Universities Power Engineering Conf. (AUPEC 2006), Melbourne, Australia, 2006.
    19. 19)
      • 18. Mohamed, O., Wang, J., Khalil, A., et al: ‘Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency’, SpringerPlus, 2016, 5, (1), p. 980.
    20. 20)
      • 32. Qi, W., Liu, J., Chen, X., et al: ‘Supervisory predictive control of standalone wind/Solar energy generation systems’, IEEE Trans. Control Syst. Technol., 2011, 19, (1), pp. 199207.
    21. 21)
      • 39. Wang, L.: ‘Model predictive control system design and implementation using MATLAB’ (Springer, London, UK, 2009).
    22. 22)
      • 5. Husek, P.: ‘PID controller design for hydraulic turbine based on sensitivity margin specifications’, Electr. Power Energy Syst., 2014, 55, pp. 460466.
    23. 23)
      • 41. Smart Grid Laboratory, Available at www.fer.unizg.hr/zvne/research/research\_labs/sglab/laboratory, accessed 12 August 2019.
    24. 24)
      • 38. Draganescu, M., Guo, S., Wojcik, J., et al: ‘Generalized predicitve control for superheated steam temperature regulation in a supercritical coal-fired power plant’, CSEE J. Power Energy Syst., 2015, 1, (1), pp. 6977.
    25. 25)
      • 29. Meral, M.E., Celik, D.: ‘A comprehensive survey on control strategies of distributed generation power systems under normal and abnormal conditions’, Annu. Rev. Control, 2019, 47, pp. 112132.
    26. 26)
      • 8. Xu, C., Qian, D.: ‘Governor design for a hydro power plant with an upstream surge tank by GA-based fuzzy reduced-order sliding mode’, Energies, 2015, 8, pp. 1344213457.
    27. 27)
      • 34. Kassem, A.M., Abdelaziz, A.Y.: ‘Reactive power control for voltage stability of standalone hybrid wind-diesel power system based on functional model predictive control’, IET Renew. Power Gener., 2014, 8, (8), pp. 887899.
    28. 28)
      • 4. Simani, S., Alvisi, S., Venturini, M.: ‘Study of the time response of a simulated hydroelectric system’, J. Phys., Conf. Series, 2014, 570, (5), pp. 114.
    29. 29)
      • 23. Faille, D., Davelaar, F., Murgey, S., et al: ‘Hierarchical model predictive control applied to hydro power valley’, Proc. Int. Feder. Autom. Control, 2012, 45, (21), pp. 295300.
    30. 30)
      • 2. Kishor, N., Saini, R.P., Singh, S.P.: ‘A review on hydro power plant models and control’, Renew. Sustain. Energy Rev., 2007, 11, pp. 776796.
    31. 31)
      • 43. Kuzle, I., Havelka, J., Pandzic, H., et al: ‘Hands-on laboratory course for future power system experts’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 19631971.
    32. 32)
      • 45. Astrom, K.J., Hagglund, T.: ‘PID controllers: theory, design and tuning’ (Instrument Society of America, Research Triangle Park, NC, 1995).
    33. 33)
      • 42. Kaunda, C.S., Kimambo, C.Z., Nielsen, T.K.: ‘A technical discussion on microhydropower technology and its turbines’, Renew. Sustain. Energy Rev., 2014, 35, pp. 445459.
    34. 34)
      • 16. Wang, G., Wu, J., Zeng, B., et al: ‘State-space model predictive control method for core power control in pressurized water reactor nuclear power stations’, Nucl. Eng. Technol., 2017, 49, pp. 134140.
    35. 35)
      • 40. Hilderth, C.: ‘A quadratic programming procedure’, Nav. Res. Logist. Q., 1957, 4, pp. 7985.
    36. 36)
      • 10. ABB‘Hydro power - Intelligent solutions for hydro governors’, 2016.
    37. 37)
      • 6. Strah, B., Kuljaca, O., Vukic, Z.: ‘Speed and active power control of hydro turbine unit’, IEEE Trans. Energy Convers., 2005, 20, (2), pp. 424434.
    38. 38)
      • 12. Camacho E, F., Bordons, C.: ‘Model predictive control’ (Springer-Verlag, New York, 1999).
    39. 39)
      • 3. Orelind, G., Wozniak, L., Medanic, J., et al: ‘Optimal PID gain schedule for hydrogenerators - design and application’, IEEE Trans. Energy Convers., 1989, 4, (3), pp. 300307.
    40. 40)
      • 30. Sguarezi Filho, A.J., Oliveira Filho, M.E., Ruppert Filho, E.: ‘A predictive power control for wind energy’, IEEE Trans. Sustain. Energy, 2011, 2, (1), pp. 18.
    41. 41)
      • 46. Jones, D.I., Mansoor, S.P., Aris, F.C., et al: ‘A standard method for specifying the response of hydroelectric plant in frequency-control mode’, Electr. Power Syst. Res., 2004, 68, (1), pp. 1932.
    42. 42)
      • 35. Aurora, C., Magni, L., Scattolini, R., et al: ‘Predictive control of thermal power plants’, Int. J. Robust Nonlinear Control, 2004, 14, (4), pp. 415433.
    43. 43)
      • 22. Maestre, J.M., et al: ‘A comparison of distributed MPC schemes on a hydro-power plant benchmark’, Opt. Control Appl. Methods, 2014, 36, pp. 306332.
    44. 44)
      • 21. Setz, C., Heinrich, A., Rostalski, P., et al: ‘Application of model predictive control to a cascade of river power plants’, Proceedings of the 17th World Congress of the Int Feder. Autom. Control, 2008, 41, (2), pp. 1197811983.
    45. 45)
      • 37. Franzosi, R., Miotti, A., Pretolani, F., et al: ‘Traditional and advanced control of coal power plants: a comparative study’. Proc. of the 2006 American Control Conf. (ACC), Minneapolis, USA, 2006.
    46. 46)
      • 20. Nieto-Chaupis, H.: ‘Prospects of model predictive control of the drum level at a 225 MW combined cycle power plant’. 2016 IEEE Ecuador Technical Chapters Meeting ETCM, Guayaquil, Ecuador, 2016.
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