OPTIMAL MULTIVARIABLE CONTROL FOR WIND ENERGY CONVERSION SYSTEMS USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

El-Mahjoub Boufounas and Aumeur El Amrani

References

  1. [1] G.M.J. Herbert, S. Iniyan, E. Sreevalsan, and S. Rajapandian,A review of wind energy technologies, Renewable & SustainableEnergy Reviews, 11(6), 2007, 1117–1145.
  2. [2] D. Jena and S. Rajendran, A review of estimation of effectivewind speed based control of wind turbines, Renewable &Sustainable Energy Reviews, 43, 2015, 1046–1062.
  3. [3] H.J. Asl and J. Yoon, Power capture optimization of variable-speed wind turbines using an output feedback controller,Renewable Energy, 86, 2016, 517–525.
  4. [4] I. Poultangari, R. Shahnazi, and M. Sheikhan, RBF neuralnetwork based PI pitch controller for a class of 5-MW windturbines using particle swarm optimization algorithm, ISATransactions, 51, 2012, 641–648.
  5. [5] B. Boukhezzar, L. Lupu, H. Siguerdidjane, and M. Hand,Multivariable control strategy for variable speed, variable pitchwind turbines, Renewable Energy, 32(8), 2007, 1273–1287.
  6. [6] B. Boukhezzar and H. Siguerdidjane, Comparison betweenlinear and nonlinear control strategies for variable speed windturbines, Control Engineering Practice, 18, 2010, 1357–1368.
  7. [7] E.B. Muhando, T. Senjyu, N. Urasaki, A. Yona, H. Kinjo, andT. Funabashi, Gain scheduling control of variable speed WTGunder widely varying turbulence loading, Renewable Energy,32(14), 2007, 2407–2423.
  8. [8] X.-J. Yao, H.-C. Guo, and Y. Li, LPV H-infinity controller design for variable-pitch variable-speed wind turbine, Power Electronics and Motion Control Conference, IPEMC ’09, Wuhan,China, 2009, 2222–2227.
  9. [9] Y. Qia and Q. Meng, The application of fuzzy PID control inpitch wind turbine, Energy Procedia, 16, 2012, 1635–1641.
  10. [10] M.Q. Duong, F. Grimaccia, S. Leva, M. Mussetta, and E.Ogliari, Pitch angle control using hybrid controller for alloperating regions of SCIG wind turbine system, RenewableEnergy, 70, 2014, 197–203.
  11. [11] J. Lee, E. Son, B. Hwang, and S. Lee, Blade pitch anglecontrol for aerodynamic performance optimization of a windfarm, Renewable Energy, 54, 2013, 124–130.
  12. [12] S. Bououden, M. Chadli, S. Filali, and A. El Hajjaji, Fuzzymodel based multivariable predictive control of a variable speedwind turbine: LMI approach, Renewable Energy, 37, 2012,434–439.
  13. [13] S. Fragoso, J. Garrido, F. V´azquez, and F. Morilla, Comparative analysis of decoupling control methodologies and multi-variable robust control for variable-speed, variable-pitch windturbines: application to a lab-scale wind turbine, Sustainability, 9(713), 2017, 1–21.
  14. [14] S. Fragoso, F.V´azquez, and F. Morilla, Practical advantagesof multivariable control strategy for off-grid variable-speedvariable-pitch (VS-VP) wind turbines, Int. Conf. RenewableEnergies and Power Quality, ICREPQ’14, Cordoba, Spain,2014, ISSN 2172-038 X N.12.
  15. [15] A. Tohidi A. Shamsaddinlou, and A.K. Sedigh, Multivariableinput–output linearization sliding mode control of DFIG basedwind energy conversion system, IEEE, Istanbul, Turkey, 2013,978-1-4673-5769-2/13.
  16. [16] Z. Yinzhu and M. Yang, The study of variable speed variablepitch controller for wind power generation systems based onsliding mode control, IEEE, Hefei, China, 2016, 978-1-4673-8644-9/16.
  17. [17] S. Rajendran and D. Jena, Validation of an integral slidingmode control for optimal control of a three blade variablespeed variable pitch wind turbine, Electrical Power and EnergySystems, 69, 2015, 421–429.
  18. [18] R. Saravanakumar and J. Debashisha, Control of variable speedvariable pitch wind turbine at above and below rated windspeed, Journal of Wind Energy, 2014(2014), 1–14 (Article ID709128), http://dx.doi.org/10.1155/2014/709128.
  19. [19] C.-M. Hong, F.-S. Cheng, and C.-H. Chen, Optimal control forvariable-speed wind generation systems using general regressionneural network, International Journal of Electrical Power andEnergy Systems, 60, 2014, 14–23.
  20. [20] C.-M. Hong and C.-H. Chen, Intelligent control of a grid-connected wind-photovoltaic hybrid power systems, International Journal of Electrical Power and Energy Systems, 55,2014, 554–561.
  21. [21] R.C. Eberhart and J. Kennedy, A new optimizer using particleswarm theory, Proc. Sixth Int. Symp. on Micro-Machine andHuman Science, Nagoya, 1995, 39–43.
  22. [22] J.-R. Zhang, J. Zhang, T.-M. Lok, and M.R. Lyu, A hybridparticle swarm optimization back-propagation algorithm forfeedforward neural network training, Applied Mathematics andComputation, 185, 2007, 1026–1037.
  23. [23] H. Zhang, L. Chen, Y. Qu, G. Zhao, and Z. Guo, Supportvector regression based on grid-search method for short-termwind power forecasting, Journal of Applied Mathematics, 2014,1–11 (ID 835791).
  24. [24] Z. Hongyu, G. Yang, X. Aoran, X. Zhanguo, C. Junchao, and Z.Ming-ju, Application of GR neural network in ultra-short termwind speed forecast, Journal of Modeling and Optimization,8(1), 2016, 28–35.
  25. [25] E.E. Elattar, Prediction of wind power based on evolutionary optimised local general regression neural network, IETGeneration Transmission & Distribution, 8(5), 2014, 916–923.
  26. [26] E. Boufounas, J. Boumhidi, N. Farhane, and I. Boumhidi,Neural network sliding mode controller for a variable speedwind turbine, Control and Intelligent Systems, 41(4), 2013,251–258.
  27. [27] Y. Zhang, S. Wang, and G. Ji, Comprehensive survey onparticle swarm optimization algorithm and its applications,Mathematical Problems in Engineering, 2015, 1–38 (Article ID931256).
  28. [28] B. Beltran, T. Ahmed-Ali, and M. Benbouzid, Sliding modepower control of variable-speed wind energy conversion systems,IEEE Transactions on Energy Conversion, 23, 2008, 551–558.
  29. [29] J.J. Slotine, Sliding controller design for non-linear systems,International Journal of Control, 40(2), 1984, 421–434.
  30. [30] Y. Yuan and J. Tang, Adaptive pitch control of wind turbinefor load mitigation under structural uncertainties, RenewableEnergy, 105, 2017, 483–494.
  31. [31] D.F. Specht, A general regression neural network, IEEE Trans-actions on Neural Networks, 2, 1991, 568–576.
  32. [32] B. Xue, X. Ma, H. Wang, J. Gu, and Y. Li, improved variable-length particle swarm optimization for structure-adjustableextreme learning machine, Control and Intelligent Systems,42(4), 2014, 1–9.
  33. [33] V. Utkin and J. Shi, Integral sliding mode in systems operatingunder uncertainty conditions, Digests 35th Annual Conf. IEEEon Decision and Control, Kobe, Japan, 1996, 4591–4596.

Important Links:

Go Back