Create New Account
Login
Search or Buy Articles
Browse Journals
Search Proceedings
Subscriptions
Submit your Paper
Submission Information
Journal Review
Recommend to Your Library
Call for Papers
Browse Journals
Browse Proceedings
OPTIMAL MULTIVARIABLE CONTROL FOR WIND ENERGY CONVERSION SYSTEMS USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
El-Mahjoub Boufounas and Aumeur El Amrani
References
[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] 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] 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] 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] 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] B. Boukhezzar and H. Siguerdidjane, Comparison betweenlinear and nonlinear control strategies for variable speed windturbines, Control Engineering Practice, 18, 2010, 1357–1368.
[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] 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] Y. Qia and Q. Meng, The application of fuzzy PID control inpitch wind turbine, Energy Procedia, 16, 2012, 1635–1641.
[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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] J.J. Slotine, Sliding controller design for non-linear systems,International Journal of Control, 40(2), 1984, 421–434.
[30] Y. Yuan and J. Tang, Adaptive pitch control of wind turbinefor load mitigation under structural uncertainties, RenewableEnergy, 105, 2017, 483–494.
[31] D.F. Specht, A general regression neural network, IEEE Trans-actions on Neural Networks, 2, 1991, 568–576.
[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] 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:
Abstract
DOI:
10.2316/Journal.201.2017.4.201-2865
From Journal
(201) Mechatronic Systems and Control (formerly Control and Intelligent Systems) - 2017
Go Back