access icon free Stability properties of non-linear model predictive control of variable speed hydropower

This study presents a method for small-signal analysis of an advanced, multi-variable control system for variable speed hydropower (VSHP) plants. A model predictive controller (MPC) optimises the power plant performance. In parallel, a virtual synchronous generator-type (VSG) converter control ensures that the VSHP contributes to virtual inertia and frequency control of the power system. The aim of the small-signal analysis is to parametrise the cost function of the MPC to minimise oscillatory modes between the VSHP hydraulic system and the power system. A state-space representation of the MPC is developed by assuming a stable steady-state operating point equal to the reference values of the MPC cost function, and that no constraints are active. This state-space representation allows for small-signal analysis of the power system, including the MPC. The results show that the modes between the hydraulic system and the power system are well-damped and negligible when the costs of deviations in the hydraulic system are low compared to the cost of deviations in the VSG power reference. Thus, these modes do not constrain the tuning of the VSG. The VSHP power output can, therefore, be optimised independently through the VSG controller to damp power oscillations and reduce frequency deviations.

Inspec keywords: stability; damping; predictive control; synchronous generators; invertors; nonlinear control systems; power generation control; frequency control; state-space methods; voltage control; power system control; power system stability

Other keywords: power plant performance; power system; model predictive controller; VSHP hydraulic system; frequency control; multivariable control system; small-signal analysis; nonlinear model predictive control; VSHP power output; state-space representation; virtual synchronous generator-type converter control; virtual inertia; VSG controller; steady-state operating point; power oscillations; VSG power reference; MPC cost function; variable speed hydropower plants

Subjects: Control of electric power systems; Optimal control; Frequency control; Voltage control; Power system control; Synchronous machines; Nonlinear control systems

References

    1. 1)
      • 5. Kishor, N., Singh, S.: ‘Nonlinear predictive control for a nnarx hydro plant model’, Neural Comput. Appl., 2007, 16, (2), pp. 101108.
    2. 2)
      • 21. Tielens, P., Van-Hertem, D.: ‘The relevance of inertia in power systems’, Renew. Sustain. Energy Rev., 2016, 55, pp. 9991009.
    3. 3)
      • 10. Azad, S.P., Iravani, R., Tate, J.E.: ‘Damping inter-area oscillations based on a model predictive control (mpc) hvdc supplementary controller’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 31743183.
    4. 4)
      • 19. Alipoor, J., Miura, Y., Ise, T.: ‘Power system stabilization using virtual synchronous generator with alternating moment of inertia’, IEEE J. Emerg. Sel. Top. Power Electron., 2015, 3, (2), pp. 451458.
    5. 5)
      • 1. Kundur, P., Balu, N.J., Lauby, M.G.: ‘Power system stability and control’, vol. 7 (McGraw-hill, New York, 1994).
    6. 6)
      • 44. Endegnanew, A.G., Uhlen, K.: ‘Global analysis of frequency stability and inertia in ac systems interconnected through an hvdc’. 2016 IEEE Int. Energy Conf. (ENERGYCON), Leuven, Belgium, 2016, pp. 16.
    7. 7)
      • 18. Reigstad, T.I., Uhlen, K.: ‘Variable speed hydropower plant with virtual inertia control for provision of fast frequency reserves’, arXiv e-prints, 2020, arXiv:2003.07062.
    8. 8)
      • 4. Zheng, Y., Zhou, J., Zhu, W., et al: ‘Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit’, Neurocomputing, 2016, 207, pp. 287299.
    9. 9)
      • 40. Díaz-González, F., Hau, M., Sumper, A., et al: ‘Participation of wind power plants in system frequency control: review of grid code requirements and control methods’, Renew. Sustain. Energy Rev., 2014, 34, pp. 551564.
    10. 10)
      • 30. Ullah, N.R., Thiringer, T., Karlsson, D.: ‘Temporary primary frequency control support by variable speed wind turbines–potential and applications’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 601612.
    11. 11)
      • 48. MATLAB: ‘version 9.5.0 (R2010a)’, (Natick, Massachusetts: The MathWorks Inc., 2018).
    12. 12)
      • 32. Wang, Y., Delille, G., Bayem, H., et al: ‘High wind power penetration in isolated power systems–assessment of wind inertial and primary frequency responses’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 24122420.
    13. 13)
      • 24. D'Arco, S., Suul, J.A., Fosso, O.B.: ‘A virtual synchronous machine implementation for distributed control of power converters in smartgrids’, Electr. Power Syst. Res., 2015, 122, pp. 180197.
    14. 14)
      • 36. Arani, M.F.M., El-Saadany, E.F.: ‘Implementing virtual inertia in DFIG-based wind power generation’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 13731384.
    15. 15)
      • 45. Faanes, A., Skogestad, S.: ‘State space realization of model predictive controllers without active constraints’, Model. Identif. Control, 2003, 24, (4), p. 231.
    16. 16)
      • 39. Wang, S., Hu, J., Yuan, X., et al: ‘On inertial dynamics of virtual-synchronous-controlled DFIG-based wind turbines’, IEEE Trans. Energy Convers., 2015, 30, (4), pp. 16911702.
    17. 17)
      • 31. Wang-Hansen, M., Josefsson, R., Mehmedovic, H.: ‘Frequency controlling wind power modeling of control strategies’, IEEE Trans. Sustain. Energy, 2013, 4, (4), pp. 954959.
    18. 18)
      • 2. Reigstad, T.I., Uhlen, K.: ‘Optimized Control of Variable Speed Hydropower for Provision of Fast Frequency Reserves’, arXiv e-prints, 2020, arXiv:2003.06262.
    19. 19)
      • 7. Beus, M., Pandžić, H.: ‘Application of model predictive control algorithm on a hydro turbine governor control’. 2018 Power Systems Computation Conf. (PSCC), Dublin, Ireland, 2018, pp. 17.
    20. 20)
      • 12. Koul, S., Tiwari, S.: ‘Model predictive control for improving small signal stability of a upfc equipped smib system’. 2011 Nirma University Int. Conf. on Engineering, Ahmedabad, Gujarat, India, 2011, pp. 16.
    21. 21)
      • 46. Muske, K.R., Rawlings, J.B.: ‘Model predictive control with linear models’, AIChE J., 1993, 39, (2), pp. 262287.
    22. 22)
      • 38. Zeni, L., Rudolph, A.J., Münster-Swendsen, J., et al: ‘Virtual inertia for variable speed wind turbines’, Wind Energy, 2013, 16, (8), pp. 12251239.
    23. 23)
      • 9. Sanz, I.M., Judge, P., Spallarossa, C., et al: ‘Effective damping support through vsc-hvdc links with short-term overload capability’. 2017 IEEE PES Innovative Smart Grid Technologies Conf. Europe (ISGT-Europe), Torino, Italy, 2017, pp. 16.
    24. 24)
      • 25. Torres, M.A.L., Lopes, L.A.C., Moran, L.A.T., et al: ‘Self-tuning virtual synchronous machine: a control strategy for energy storage systems to support dynamic frequency control’, IEEE Trans. Energy Convers., 2014, 29, pp. 833840.
    25. 25)
      • 41. Wilches-Bernal, F., Chow, J.H., Sanchez-Gasca, J.J.: ‘A fundamental study of applying wind turbines for power system frequency control’, IEEE Trans. Power Syst., 2015, 31, (2), pp. 14961505.
    26. 26)
      • 27. Serban, I., Ion, C.P.: ‘Microgrid control based on a grid-forming inverter operating as virtual synchronous generator with enhanced dynamic response capability’, Int. J. Electri. Power Energy Syst., 2017, 89, pp. 84105.
    27. 27)
      • 20. Poolla, B.K., Groß, D., Dörfler, F.: ‘Placement and implementation of grid-forming and grid-following virtual inertia and fast frequency response’, IEEE Trans. Power Syst., 2019, 34, (4), pp. 30353046.
    28. 28)
      • 14. Ersdal, A.M., Cecilio, I.M., Fabozzi, D., et al: ‘Applying model predictive control to power system frequency control’. 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), Lyngby, Denmark, 2013, pp. 15.
    29. 29)
      • 11. Jain, A., Biyik, E., Chakrabortty, A.: ‘A model predictive control design for selective modal damping in power systems’. American Control Conf. (ACC), Chicago, Illinois, 2015, pp. 43144319.
    30. 30)
      • 16. Ersdal, A.M., Imsland, L., Uhlen, K., et al: ‘Model predictive load–frequency control taking into account imbalance uncertainty’, Control Eng. Practice, 2016, 53, pp. 139150.
    31. 31)
      • 35. Soni, N., Doolla, S., Chandorkar, M.C.: ‘Improvement of transient response in microgrids using virtual inertia’, IEEE Trans. Power Deliv., 2013, 28, (3), pp. 18301838.
    32. 32)
      • 34. Brisebois, J., Aubut, N.: ‘Wind farm inertia emulation to fulfill hydro-québec's specific need’. 2011 IEEE Power and Energy Society General Meeting, Detroit, Michigan, USA, 2011.
    33. 33)
      • 15. Ersdal, A.M., Imsland, L., Uhlen, K.: ‘Model predictive load-frequency control’, IEEE Trans. Power Syst., 2016, 31, (1), pp. 777785.
    34. 34)
      • 26. Mo, O., D'Arco, S., Suul, J.A.: ‘Evaluation of virtual synchronous machines with dynamic or quasi-stationary machine models’, IEEE Trans. Ind. Electron., 2017, 64, (7), pp. 59525962.
    35. 35)
      • 17. Elsisi, M., Soliman, M., Aboelela, M., et al: ‘Improving the grid frequency by optimal design of model predictive control with energy storage devices’, Optim. Control Appl. Methods, 2018, 39, (1), pp. 263280.
    36. 36)
      • 37. Zhu, J., Booth, C.D., Adam, G.P., et al: ‘Inertia emulation control strategy for VSC-HVDC transmission systems’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 12771287.
    37. 37)
      • 33. Wu, L., Infield, D.G.: ‘Towards an assessment of power system frequency support from wind plant–modeling aggregate inertial response’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 22832291.
    38. 38)
      • 8. Fuchs, A., Imhof, M., Demiray, T., et al: ‘Stabilization of large power systems using vsc–hvdc and model predictive control’, IEEE Trans. Power Deliv., 2014, 29, (1), pp. 480488.
    39. 39)
      • 28. Liu, J., Miura, Y., Ise, T.: ‘Comparison of dynamic characteristics between virtual synchronous generator and droop control in inverter-based distributed generators’, IEEE Trans. Power Electron., 2016, 31, (5), pp. 36003611.
    40. 40)
      • 42. Bao, W., Wu, Q., Ding, L., et al: ‘Synthetic inertial control of wind farm with BESS based on model predictive control’, IET Renew. Power Gener., 2020, 14, (13), pp. 24472455.
    41. 41)
      • 47. Lewis, F.L., Xie, L., Popa, D.: ‘Optimal and robust estimation: with an introduction to stochastic control theory’ (CRC press, Florida, USA, 2017).
    42. 42)
      • 23. Tamrakar, U., Shrestha, D., Maharjan, M., et al: ‘Virtual inertia: current trends and future directions’, Appl. Sci., 2017, 7, (7), p. 654.
    43. 43)
      • 13. Imhof, M., Fuchs, A., Andersson, G., et al: ‘Voltage stability control using vsc-hvdc links and model predictive control’. XIII Symp. of Specialists in Electric Operational and Expension Planning, XIII SEPOPE, Foz do Iguassu, Brazil, 2014.
    44. 44)
      • 43. Reigstad, T.I., Uhlen, K.: ‘Variable speed hydropower conversion and control’, IEEE Trans. Energy Convers., 2020, 35, (1), pp. 386393.
    45. 45)
      • 6. Zhang, H., Chen, D., Xu, B., et al: ‘Nonlinear modeling and dynamic analysis of hydro-turbine governing system in the process of load rejection transient’, Energy Convers. Manage., 2015, 90, pp. 128137.
    46. 46)
      • 22. D'Arco, S., Suul, J.A., Fosso, O.B.: ‘Small-signal modeling and parametric sensitivity of a virtual synchronous machine in islanded operation’, Int. J. Electr. Power Energy Syst., 2015, 72, pp. 315.
    47. 47)
      • 29. Sakimoto, K., Miura, Y., Ise, T.: ‘Stabilization of a power system with a distributed generator by a virtual synchronous generator function’. 2011 IEEE 8th Int. Conf. on Power Electronics and ECCE Asia (ICPE & ECCE), Jeju, South Korea, 2011.
    48. 48)
      • 49. Beerten, J., D'Arco, S., Suul, J.A.: ‘Identification and small-signal analysis of interaction modes in vsc mtdc systems’, IEEE Trans. Power Deliv., 2016, 31, (2), pp. 888897.
    49. 49)
      • 3. Reigstad, T.I., Uhlen, K.: ‘Nonlinear Model Predictive Control of Variable Speed Hydropower for Provision of Fast Frequency Reserves’, arXiv e-prints, 2020, arXiv:2006.02097.
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