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

access icon free Distributed model predictive control for load frequency control with dynamic fuzzy valve position modelling for hydro–thermal power system

  • Author(s): Xiangjie Liu 1 ; Xiaobing Kong 1 ; Kwang Y. Lee 2
    • View affiliations
    • Affiliations: 1: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, People's Republic of China ;
      2: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798-7356, USA
  • Source: Volume 10, Issue 14, 19 September 2016, p. 1653 – 1664
    DOI:  10.1049/iet-cta.2015.1021 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 23/10/2015, Accepted 24/03/2016, Revised 29/02/2016, Published 09/06/2016

Reliable load frequency control (LFC) is very important for a modern power system with multi-source power generation and has been the primary focus of studies on advanced control theory and applications. In the LFC of a power system, the generation rate constraints (GRC) and position limit of the governor valve present major challenges to the control scheme because they significantly affect the dynamic responses of the system, resulting in larger overshoot and longer settling time. Model predictive control (MPC) is an attractive control strategy that systematically considers the constraints on the process inputs, states, and outputs. It is employed in LFC to cope with the GRC problem. This study proposes a distributed MPC (DMPC) for a four-area hydro–thermal interconnected power system. In the proposed scheme, the limit position of the governor valve is modelled by a fuzzy model and the local predictive controllers are incorporated into the non-linear control system. The effectiveness of the proposed non-linear constraint DMPC was demonstrated by simulations.

Inspec keywords: position control; distributed control; power generation control; load regulation; hydrothermal power systems; frequency control; fuzzy control; valves; power system interconnection; predictive control; fuzzy set theory

Other keywords: LFC; local predictive controllers; reliable load frequency control; nonlinear control system; DMPC; advanced control theory; multisource power generation; dynamic fuzzy valve position modelling; governor valve position limit; four-area hydro-thermal interconnected power system; distributed model predictive control; generation rate constraints; power system

Subjects: Power system control; Control of electric power systems; Fuzzy control; Combinatorial mathematics; Combinatorial mathematics; Actuating and final control devices; Power system management, operation and economics; Frequency control; Optimal control; Spatial variables control; Hydroelectric power stations and plants; Thermal power stations and plants

References

    1. 1)
      • 18. Zhou, X.J., Li, C.J., Huang, T.W., et al: ‘Fast gradient-based distributed optimisation approach for model predictive control and application in four-tank benchmark’, IET Control Theory Appl., 2015, 9, (10), pp. 1579–1586.
    2. 2)
      • 9. Wang, Z.W., Wang, X.D., Liu, L.H., et al: ‘Optimal state feedback control for wireless networked control systems with decentralised controllers’, IET Control Theory Appl., 2015, 9, (6), pp. 852–862.
    3. 3)
      • 27. Lee, H.J., Park, J.B., Joo, Y.H.: ‘Robust load-frequency control for uncertain nonlinear power systems: a fuzzy logic approach’, Inf. Sci., 2006, 176, (23), pp. 227–231.
    4. 4)
      • 8. Sahu, B.K., Pati, S., Panda, S.: ‘Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system’, IET Gener. Transm. Distrib., 2014, 8, (11), pp. 1789–1800.
    5. 5)
      • 5. Vrdoljak, K., Peric, N., Petrovic, I.: ‘Sliding mode based load-frequency control in power systems’, Electr. Power Syst. Res., 2010, 80, (5), pp. 514–527.
    6. 6)
      • 23. Lee, K.Y., Belbachir, M.: ‘A decentralized plant controller for automatic generation and voltage regulation’, J. Electr. Power Syst. Res., 1982, 5, (1), pp. 41–51.
    7. 7)
      • 16. Liu, X.J., Kong, X.B.: ‘Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system’, J. Process Control, 2013, 23, (8), pp. 1023–1040.
    8. 8)
      • 12. Qin, S.J., Badgwell, T.A.: ‘A survey of industrial model predictive control technology’, Control Eng. Pract., 2003, 11, (7), pp. 733–764.
    9. 9)
      • 26. Slotine, J.E.: ‘Applied nonlinear control’ (Prentice-Hall, Englewood Cliffs, 1991).
    10. 10)
      • 32. Doolla, S., Bhatti, T.S.: ‘Load frequency control of an isolated small-hydro power plant with reduced dump load’, IEEE Trans. Power Syst., 2006, 21, (4), pp. 1912–1919.
    11. 11)
      • 20. Song, Y., Fang, X.S.: ‘Distributed model predictive control for polytopic uncertain systems with randomly occurring actuator saturation and packet loss’, IET Control Theory Appl., 2014, 8, (5), pp. 297–310.
    12. 12)
      • 29. Panda, G., Panda, S., Ardil, C.: ‘Automatic generation control of interconnected power system with generation rate constraints by hybrid neuro fuzzy approach’, Int. J. Electr. Robot. Electron. Commun. Eng., 2012, 6, (4), pp. 27–32.
    13. 13)
      • 10. Pandey, S.K., Mohanty, S.R., Kishor, N.: ‘A literature survey on load of frequency control for conventional and distribution generation power systems’, Renew. Sustain. Energy Rev., 2013, 25, pp. 318–334.
    14. 14)
      • 33. Zhao, Y., Chen, X., Jia, Q., et al: ‘Long-term scheduling for cascaded hydro energy systems with annual water consumption and release constraints’, IEEE Trans. Autom. Sci. Eng., 2010, 7, (4), pp. 969–976.
    15. 15)
      • 25. Mohamed, T.H., Bevrani, H., Hassan, A.A., et al: ‘Decentralized model predictive based load frequency control in an interconnected power system’, Energy Convers. Manag., 2011, 52, (2), pp. 1208–1214.
    16. 16)
      • 4. Yamashita, K., Miyagi, H.: ‘Multivariable self-tuning regulator for load frequency control system with interaction of voltage on load demand’, IEE Proc. D, Control Theory Appl., 1991, 138, (2), pp. 177–183.
    17. 17)
      • 21. Hashimoto, K., Adachi, S., Dimarogonas, D.V.: ‘Distributed aperiodic model predictive control for multi-agent systems’, IET Control Theory Appl., 2015, 9, (1), pp. 10–20.
    18. 18)
      • 34. Liu, X., Kong, X., Deng, X.: ‘Power system model predictive load frequency control’. Proc. of the American Control Conf., Montreal, Canada, 27–29 June 2012, pp. 6602–6607.
    19. 19)
      • 15. Liu, X.J., Chan, C.: ‘Neuro-fuzzy generalized predictive control of boiler steam temperature’, IEEE Trans. Energy Convers., 2006, 21, (4), pp. 900–9008.
    20. 20)
      • 6. Rahmani, M., Sadati, N.: ‘Hierarchical optimal robust load-frequency control for power systems’, IET Gener. Transm. Distrib., 2012, 6, (4), pp. 303–312.
    21. 21)
      • 14. Liu, X.J., Guan, P., Chan, C.W.: ‘Nonlinear multivariable power plant coordinate control by constrained predictive scheme’, IEEE Trans. Control Syst. Technol., 2010, 18, (5), pp. 1116–1125.
    22. 22)
      • 11. Zheng, Y., Qiu, H., Li, S.: ‘Networked coordination-based distributed model predictive control for large-scale system’, IEEE Trans. Control Syst. Technol., 2013, 21, (3), pp. 991–998.
    23. 23)
      • 30. Liu, X., Zhan, X., Qian, D.: ‘Load frequency control considering generation rate constraints’. Proc. of the 8th World Congress on Intelligent Control and Automation, Jinan, China, 2010, pp. 1394–1401.
    24. 24)
      • 28. Shayeghi, H., Shayanfar, H.A.: ‘Application of ANN technique based on μ-synthesis to load frequency control of interconnected power system’, Int. J. Electr. Power Energy Syst., 2006, 28, (7), pp. 503–511.
    25. 25)
      • 13. Prasad, G., Swidenbank, E., Hogg, B.W.: ‘A neural net model-based multivariable long-range predictive control strategy applied in thermal power plant control’, IEEE Trans. Energy Convers., 1998, 13, (2), pp. 176–182.
    26. 26)
      • 31. Rerkpreedapong, D., Atic, N., Feliachi, A.: ‘Economy oriented model predictive load frequency control’. 2003 Large Engineering Systems Conf. on Power Engineering, Montreal, Canada, 2003, pp. 12–16.
    27. 27)
      • 22. Christofides, P.D., Scattolini, R., Munoz de la Pena, D., et al: ‘Distributed model predictive control: a tutorial review and future research directions’, Comput. Chem. Eng., 2013, 51, (5), pp. 21–24.
    28. 28)
      • 17. Zhou, L.F., Li, S.Y.: ‘Distributed model predictive control for consensus of sampled-data multi-agent systems with double-integrator dynamics’, IET Control Theory Appl., 2015, 9, (12), pp. 1774–1780.
    29. 29)
      • 1. Geromel, J.C., Peres, P.L.D.: ‘Decentralised load-frequency control’, IEE Proc. D, Control Theory Appl., 1985, 132, (5), pp. 225–230.
    30. 30)
      • 3. Hasan, N., Ibraheem, K., Kumar, P.: ‘Optimal automatic generation control of interconnected power system considering new structures of matrix Q’, Elect. Power Compon. Syst., 2013, 41, (2), pp. 136–156.
    31. 31)
      • 7. Daneshfar, F.: ‘Intelligent load-frequency control in a deregulated environment: continuous-valued input, extended classifier system approach’, IET Gener. Transm. Distrib., 2013, 7, (6), pp. 551–559.
    32. 32)
      • 24. Venkat, A.N., Hiskens, I.A., Rawlings, J.B., et al: ‘Distributed MPC strategies with application to power system automatic generation control’, IEEE Trans. Control Syst. Technol., 2008, 16, (6), pp. 1192–1206.
    33. 33)
      • 2. Farahani, M., Ganjefar, S., Alizadeh, M.: ‘PID controller adjustment using chaotic optimisation algorithm for multi-area load frequency control’, IET Control Theory Appl., 2012, 6, (13), pp. 1984–1992.
    34. 34)
      • 19. Giovanini, L.: ‘Game approach to distributed model predictive control’, IET Control Theory Appl., 2011, 5, (15), pp. 1729–1739.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2015.1021
Loading

Related content

content/journals/10.1049/iet-cta.2015.1021
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address