Advertisement

Linear-Quadratic Regulator Algorithm-Based Cascaded Control Scheme for Performance Enhancement of a Variable-Speed Wind Energy Conversion System

  • Mahmoud A. Soliman
  • Hany M. Hasanien
  • Haitham Z. Azazi
  • E. E. El-Kholy
  • S. A. Mahmoud
Research Article - Electrical Engineering
  • 15 Downloads

Abstract

Wind power generation is an important trend of global electricity generation. Enormous efforts should be spent to keep the operation of the wind power systems at optimal conditions. This paper presents a linear-quadratic regulator (LQR) algorithm based on an optimal control scheme for enhancing the characteristics of the wind turbine generator systems (WTGSs). The variable-speed wind turbine driving a permanent-magnet synchronous is connected to the electric network through fully controlled power converters. The machine-side converter and the grid-side inverter are controlled using a cascaded LQR control scheme. LQR is an optimal controller, which achieves a rapid convergence and less computational complexity. The modelling and the control strategies of the system under study are elucidated in details. Real wind speed data captured from Zaafarana wind farm, Egypt, are taken into account for obtaining realistic responses. The effectiveness of the proposed controller is compared with that achieved using the genetic algorithm-based optimized proportional-plus-integral controller, considering the network disturbances. The simulation results are carried out using MATLAB/Simulink software that validate the efficiency of the proposed control scheme for enhancing the characteristics of the WTGSs connected to electric networks.

Keywords

Frequency converter Linear-quadratic regulator (LQR) Permanent-magnet synchronous generator (PMSG) Wind energy conversion system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Global Wind Energy Council (GWEC): Annual market update 2015. Available at http://www.gwec.net, Accessed 31st Dec 2016
  2. 2.
    Global Wind Energy Council (GWEC): Global wind energy outlook 2016. Available at: http://www.gwec.net. Accessed 31st Dec 2017
  3. 3.
    Zeng, X.; Yao, J.; Chen, Z.; Hu, W.; Chen, Z.; Zhou, T.: Co-ordinated control strategy for hybrid wind farms with PMSG and FSIG under unbalanced grid voltage condition. IEEE Trans. Sustain. Energy 7(3), 1100–1110 (2016)CrossRefGoogle Scholar
  4. 4.
    Hasanien, Hany M.; Muyeen, S.M.: Affine projection algorithm based adaptive control scheme for operation of variable-speed wind generator. IET Gener. Transm. Distrib. 9(16), 2611–2616 (2015)CrossRefGoogle Scholar
  5. 5.
    Moataz, A.; Mohammed, E.A.: Enhanced flicker mitigation in DFIG-based distributed generation of wind power. IEEE Trans. Ind. Inf. 12(6), 2041–2049 (2016)CrossRefGoogle Scholar
  6. 6.
    Rahmanian, E.; Akbari, H.; Sheisi, G.H.: Maximum power point tracking in grid connected wind plant by using intelligent controller and switched reluctance generator. IEEE Trans. Sustain. Energy 8(3), 1313–1320 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhang, Z.; Fang, H.; Gao, F.; Rodr’ıguez, J.; Kennel, R.: Multiple-vector model predictive power control for grid-tied wind turbine system with enhanced steady state control performance. IEEE Trans. Ind. Electron. 64(8), 6287–6298 (2017)CrossRefGoogle Scholar
  8. 8.
    Benadja, M.; Chandra, A.: Adaptive sensorless control of PMSGs based offshore wind farm and VSC-HVDC stations. IEEE J. Emerg. Sel. Top. Power Electron. 3(4), 918–931 (2015)CrossRefGoogle Scholar
  9. 9.
    Muyeen, S.M.; Al-Durra, Ahmed: Modeling and control strategies of fuzzy logic controlled inverter system for grid interconnected variable speed wind generator. IEEE Syst. J. 7(4), 817–824 (2013)CrossRefGoogle Scholar
  10. 10.
    Rad, A.B.; Lo, W.L.; Tsang, K.M.: Self-tuning PID controller using Newton–Raphson search method. IEEE Trans. Ind. Electron. 44(5), 717–725 (1997)CrossRefGoogle Scholar
  11. 11.
    Li, Y.; Ang, K.H.; Chong, G.C.Y.: PID control system analysis and design, problems, remedies, and future directions. IEEE Control Syst. Mag. 26(1), 32–41 (2006)CrossRefGoogle Scholar
  12. 12.
    Hasanien, H.M.: Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method. IEEE Syst. J. 7(4), 825–831 (2013)CrossRefGoogle Scholar
  13. 13.
    Passino, K.M.: Biomimicry of bacterial foraging for distribute optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hasanien Hany, M.: Particle swarm design optimization of transverse flux linear motor for weight reduction and improvement of thrust force. IEEE Trans. Ind. Electron. 58(9), 4048–4056 (2011)CrossRefGoogle Scholar
  15. 15.
    Islam, Gazi; Muyeen, S.M.; Al-Durra, Ahmed; Hasanien Hany, M.: RTDS implementation of an improved sliding mode based inverter controller for PV system. ISA Trans. 62, 50–59 (2016)CrossRefGoogle Scholar
  16. 16.
    Hasanien Hany, M.: An adaptive control strategy for low voltage ride through capability enhancement of grid connected photovoltaic power plants. IEEE Trans. Power Syst. 31(4), 3230–3237 (2016)CrossRefGoogle Scholar
  17. 17.
    Taj, T.A.; Hasanien, H.M.; Alolah, A.I.; Muyeen, S.M.: Transient stability enhancement of a grid-connected wind farm using an adaptive neuro-fuzzy controlled-flywheel energy storage system. IET Renew. Power Gener. 9(7), 792–800 (2015)CrossRefGoogle Scholar
  18. 18.
    Muyeen, S.M.; Hasanien, H.M.; Tamura, J.: Reduction of frequency fluctuation for wind farm connected power systems by an adaptive artificial neural network controlled energy capacitor system. IET Renew. Power Gener. 6(4), 226–235 (2012)CrossRefGoogle Scholar
  19. 19.
    Muyeen, S.M.; Hasanien Hany, M.; Al-Durra, A.: Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES. Energy Convers. Manag. 78(4), 412–420 (2014)CrossRefGoogle Scholar
  20. 20.
    Hasanien Hany, M.; Muyeen, S.M.: Design optimization of controller parameters used in variable-speed wind energy conversion system by genetic algorithms. IEEE Trans. Sustain. Energy 3(2), 200–208 (2012)CrossRefGoogle Scholar
  21. 21.
    Hasanien Hany, M.; Muyeen, S.M.: A Taguchi approach for optimum design of proportional-integral controllers in cascaded control scheme. IEEE Trans. Power Syst. 28(2), 1636–1644 (2013)CrossRefGoogle Scholar
  22. 22.
    Ambia, M.N.; Hasanien Hany, M.; Muyeen, S.M.: Harmony search algorithm-based controller parameters optimization for a distributed-generation system. IEEE Trans. Power Del. 30(1), 246–255 (2015)CrossRefGoogle Scholar
  23. 23.
    Hasanien Hany, M.: Shuffled frog leaping algorithm-based static synchronous compensator for transient stability improvement of a grid-connected wind farm. IET Renew. Power Gener. 8(6), 722–730 (2014)CrossRefGoogle Scholar
  24. 24.
    Hasanien Hany, M.: A set-membership affine projection algorithm-based adaptive-controlled SMES units for wind farms output power smoothing. IEEE Trans. Sustain. Energy 5(4), 1226–1233 (2014)CrossRefGoogle Scholar
  25. 25.
    Kalaam, Rahila N.; Muyeen, S.M.; Al-Durra, A.; Hasanien Hany, M.; Al-Wahedi, Khaled: Optimisation of controller parameters for grid-tied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm. IET Renew. Power Gener. 11(12), 1517–1526 (2017)CrossRefGoogle Scholar
  26. 26.
    Olalla, C.; Leyva, R.; El Aroudi, A.; Queinnec, I.: Robust LQR control for PWM converters: an LMI approach. IEEE Trans. Ind. Electron. 56(7), 2548–2558 (2009)CrossRefGoogle Scholar
  27. 27.
    Nallusamy, S.; Velayutham, D.; Govindarajan, Uma: Design and implementation of a linear quadratic regulator controlled active power conditioner for effective source utilisation and voltage regulation in low-power wind energy conversion systems. IET Power Electron. 8(11), 2145–2155 (2015)CrossRefGoogle Scholar
  28. 28.
    Rubio, J.J.; Lopez, J.; Pacheco, J.; Encinas, R.: Control of two electrical plants. Asian J. Control 20(5), 1–15 (2018)MathSciNetGoogle Scholar
  29. 29.
    Pan, Y.; Sun, T.; Yu, H.: Composite adaptive dynamic surface control using online recorded data. Int. J. Robust Nonlinear Control 26(18), 3921–3936 (2016)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Rubio, J.J.; Ochoa, G.; Balcazar, R.; Pacheco, J.: Uniform stable observer for the disturbance estimation in two renewable energy systems. ISA Trans. 58, 155–164 (2015)CrossRefGoogle Scholar
  31. 31.
    Pan, Y.; Liu, Y.; Xu, B.; Yu, H.: Hybrid feedback feed forward: an efficient design of adaptive neural network control. Neural Netw. 76, 122–134 (2016)CrossRefGoogle Scholar
  32. 32.
    Rubio, J.J.: Robust feedback linearization for nonlinear processes control. ISA Trans. 74, 155–164 (2018)CrossRefGoogle Scholar
  33. 33.
    Islam, F.; Hasanien, H.; Al-Durra, A.; Muyeen, S. M.: A new control strategy for smoothing of wind farm output using short-term ahead wind speed prediction and flywheel energy storage system. In: Proceeding of American Control Conference, ACC, Montreal, Canada, pp. 3026–3031 (2012)Google Scholar
  34. 34.
    Thongam, J. S.; Bouchard, Ezzaidi, P.H.; Ouhrouche, M.: Wind speed sensorless maximum power point tracking control of variable speed wind energy conversion systems. In: IEEE International Conference Electric Machines and Drives Conference (IEMDC), Miami, USA, pp. 1832–1837 (2009)Google Scholar
  35. 35.
    Ali Hasan, M.; Wu, B.: Comparison of stabilization methods for fixed-speed wind generator systems. IEEE Trans. Power Deliv. 25(1), 323–331 (2010)CrossRefGoogle Scholar
  36. 36.
    Prasad, L.B.; Tyagi, B.; Gupta, H.O.: Optimal control of nonlinear inverted pendulum dynamical system with disturbance input using PID controller and LQR. In: IEEE International Conference on Control System Computing and Engineering, pp. 540–545 (2012)Google Scholar
  37. 37.
    Mohammadbagher, A.; Zaeri, N.; Yaghoobi, M.: Comparison performance between PID and LQR controller for 4-leg voltage source inverter. In: International Conference on Circuit, System, and Simulation, pp. 230–234 (2011)Google Scholar
  38. 38.
    Ogata, K.: Modern Control Engineering. PHI Learning Private Limited, New Delhi (2011)MATHGoogle Scholar
  39. 39.
    Pradhan, J.K.; Ghosh, A.: Multi-input and multi-output proportional-integral-derivative controller design via linear quadratic regulator-linear matrix inequality approach. IET Control Theory Appl. 9(14), 2140–2145 (2015)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Haque, MdE; Negnevitsky, M.; Muttaqi, K.M.: A novel control strategy for a variable-speed wind turbine with a permanent-magnet synchronous generator. IEEE Trans. Ind. Appl. 46(1), 331–339 (2010)CrossRefGoogle Scholar
  41. 41.
    Goldbert, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Reading. Addison-Wesley, Boston (1989)Google Scholar
  42. 42.
    Cabral, H.A.; Melo, M.T.: Using genetic algorithms for device modeling. IEEE Trans. Magn. 47(5), 1322–1325 (2011)CrossRefGoogle Scholar
  43. 43.
    Kassem, Ahmed M.: Modelling and robust control design of a standalone wind-based energy storage generation unit powering an induction motor-variable-displacement pressure-compensated pump. IET Renew. Power Gener. 10(3), 275–286 (2016)CrossRefGoogle Scholar
  44. 44.
    Zhang, Z.; Zhao, Y.; Qiao, W.; Qu, L.: A space-vector-modulated sensorless direct-torque control for direct-drive PMSG wind turbines. IEEE Trans. Ind. Appl. 50(4), 2331–2341 (2014)CrossRefGoogle Scholar
  45. 45.
    Errouissi, Rachid; Muyeen, S.M.; Al-Durra, Ahmed; Leng, Siyu: Experimental validation of a robust continuous nonlinear model predictive control based grid-interlinked photovoltaic inverter. IEEE Trans. Ind. Electron. 63(7), 4495–4505 (2016)CrossRefGoogle Scholar
  46. 46.
    Kim, Y.-S.; Chung, I.-Y.; Moon, S.I.: Tuning of the PI controller parameters of a PMSG wind turbine to improve control performance under various wind speeds. Energies J. 8, 406–1425 (2015)Google Scholar
  47. 47.
    Chung, I.-Y.; Liu, W.; Cartes, D.A.; Collins, E.G.; Moon, S.-I.: Control methods of inverter-interfaced distributed generators in a micro grid system. IEEE Trans. Ind. Appl. 46(3), 1078–1088 (2010)CrossRefGoogle Scholar
  48. 48.
    Genetic Algorithm and Direct Search ToolboxTM User’s Guide, Release 2013b. The Math Works Press (2013)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Mahmoud A. Soliman
    • 1
  • Hany M. Hasanien
    • 2
  • Haitham Z. Azazi
    • 1
  • E. E. El-Kholy
    • 1
  • S. A. Mahmoud
    • 1
  1. 1.Electrical Engineering Department, Faculty of EngineeringMenoufiya UniversityShebin El-KomEgypt
  2. 2.Electrical Power and Machines Department, Faculty of EngineeringAin Shams UniversityCairoEgypt

Personalised recommendations