Adaptive and predictive controllers applied to onshore wind energy conversion system

Carla Viveiros; Rui Melicio; Victor Mendes; Jose Igreja; Carla Viveiros; Rui Melicio; Victor Mendes; Jose Igreja

doi:10.3934/energy.2018.4.615

AIMS Energy

2018, Volume 6, Issue 4: 615-631. doi: 10.3934/energy.2018.4.615

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Research article

Adaptive and predictive controllers applied to onshore wind energy conversion system

1.
ICT, Universidade de Évora, Évora, Portugal
2.
Departamento de Física, Escola de Ciências e Tecnologia, Universidade de Évora, Portugal
3.
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
4.
Instituto Superior de Engenharia de Lisboa, Lisboa, Portugal
5.
CISE, Electromechatronic Systems Research Centre, Universidade da Beira Interior, Portugal
6.
INESC-ID, Lisboa, Portugal

Received: 31 May 2018 Accepted: 13 August 2018 Published: 28 August 2018

This paper presents a simulation of onshore energy conversion system connected to the electric grid and under an event-based supervisor control based on deterministic version of a finite state machine. The onshore energy conversion system is composed by a variable speed wind turbine, a mechanical transmission system described by a two-mass drive train, a gearbox, a doubly fed induction generator rotor and by a two-level converter. First, mathematical models of a variable speed wind turbine with pitch control are studied, followed by the study of different controller types such as adaptive controllers and predictive controllers. The study of an event-based supervisor based on finite state machines is also studied. The control and supervision strategy proposed for the onshore energy conversion system is based on a hierarchical structure with two levels, execution level where the adaptive and predictive controllers are included, and the supervision level where the event-based supervisor is included. The objective is to control the electric output power around the reference power and also to analyze the operational states according to the wind speed. The studied mathematical models are integrated into computer simulations for the onshore energy conversion system and the obtained numerical results allow for the performance assessment of the system connected to the electric grid. A comparison of the onshore energy conversion system performance without or with the supervisor is carried out to access the influence of the control and supervision strategy on the performance.

Keywords:

Citation: Carla Viveiros, Rui Melicio, Victor Mendes, Jose Igreja. Adaptive and predictive controllers applied to onshore wind energy conversion system[J]. AIMS Energy, 2018, 6(4): 615-631. doi: 10.3934/energy.2018.4.615

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Abstract

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References

[1]	World Wind Energy Association (2018) Wind Power Capacity reaches 539 GW, 52,6 GW added in 2017. Available from: http://www.wwindea.org/2017-statistics/
[2]	Vidal Y, Acho L, Luo N, et al. (2012) Power control design for variable-speed wind turbines. Energies 5: 3033–3050. doi: 10.3390/en5083033
[3]	Garcia-Sanz M (2012) Wind Energy Systems: Control Engineering Design. CRC Press.
[4]	Melicio R, Mendes VMF (2005) Doubly fed induction generator systems for variable speed wind turbine. In Proc. 9th Spanish-Portuguese Congress on Electrical Engineering-9CHLIE, Marbella, Spain, 161–164.
[5]	Siraj K, Siraj H, Nasir M (2014) Modeling and control of a doubly fed induction generator for grid integrated wind turbine. In IEEE International Power Electronics and Motion Control Conference and Exposition-PEMC 2014, Antalya, Turkey, 901–906.
[6]	Zhang J, Cheng M, Chen,Z, et al. (2008) Pitch angle control for variable speed wind turbines. In 3rd International Conference on Electric Utility Deregulation and Restructuring and Power Technologies-DRPT 2008, Nanjuing, 2691–2696.
[7]	Merabet A, Thongam J, Gu J (2011) Torque and pitch angle control for variable speed wind turbines in all operating regimes. In 10th International Conference on Environment and Electrical Engineering-EEEIC 2011, Rome, Italy, 1–5.
[8]	Lupu L, Boukhezzar B, Siguerdidjane H (2006) Pitch and torque control strategy for variable speed wind turbines. In European Wind Energy Conference & Exhibition-EWEC 2006, Athens, Greece, 1–7.
[9]	Bianchi FD, Battista HD, Mantz RJ (2010) Robust multivariable gain-scheduled control of wind turbines for variable power production. Int J Syst Control 1: 103–112.
[10]	Aissaoui AG, Tahour A, Essounbouli N, et al. (2013) A fuzzy-pi control to extract an optimal power from wind turbine. Energ Convers Manage 65: 688–696. doi: 10.1016/j.enconman.2011.11.034
[11]	Mateescu R, Pintea A, Stefanoiu D (2012) Discrete-time LQG control with disturbance rejection for variable speed wind turbines. In 1st International Conference on Systems and Computer Science-ICSCS 2012, Lille, France, 1–6.
[12]	Simani S, Castaldi P (2013) Data-driven and adaptive control applications to a wind turbine benchmark model. Control Eng Pract 21: 1678–1693. doi: 10.1016/j.conengprac.2013.08.009
[13]	Beltran B, Ahmed-Ali T, Benbouzid MEH (2008) Sliding mode power control of variable-speed wind energy conversion systems. IEEE T Energy Conver 23: 551–558. doi: 10.1109/TEC.2007.914163
[14]	Tchakoua P, Wamkeue R, Ouhrouche M, et al. (2014) Wind turbine condition monitoring: State-of-the-art review. Energies 7: 2595–2630. doi: 10.3390/en7042595
[15]	Yang W, Jiang J (2011) Wind turbine condition monitoring and reliability analysis by SCADA information. In IEEE International Conference on Mechanic Automation and Control Engineering-MACE 2011, Hohhot, China, 1872–1875.
[16]	Qi W, Liu J, Chen X, et al. (2011) Supervisory predictive control of standalone wind/solar energy generation systems. IEEE T Contr Syst T 19: 199–207. doi: 10.1109/TCST.2010.2041930
[17]	Sarrias R, Fernández LM, García CA, et al. (2011) Supervisory control system for DFIG wind turbine with energy storage system based on battery. In: International Conference on Power Engineering, Energy and Electrical Drives-POWERENG, Malaga, Spain, 1–6.
[18]	Johnson KE, Pao LY, Balas MJ, et al. (2006) Control of variable-speed wind turbines: Standard and adaptive techniques for maximizing energy capture. IEEE Control Syst 26: 70–81.
[19]	Odgaard PF, Stroustrup J, Kinnaert M (2013) Fault tolerant control of wind turbines: A benchmark model. IEEE T Contr Syst T 12: 1168–1182.
[20]	Melicio R, Mendes VMF, Catalão JPS (2010) Wind turbines equipped with fractional-order controllers: Stress on the mechanical drive train due to a converter control malfunction. Wind Energy 14: 13–25.
[21]	Melicio R, Mendes VMF, Catalão JPS (2008) Two-level and multilevel converters for wind energy systems: A comparative study. In Proc. 13th International Power Electronics and Motion Control Conference-EPE-PEMC 2008, Poznań, Poland, 1682–1687.
[22]	Melicio R, Mendes VMF, Catalão JPS (2010) Modeling, control and simulation of full-power converter wind turbines equipped with permanent magnet synchronous generator. Int Rev Electr Eng-I 5: 397–408.
[23]	Melicio R, Mendes VMF, Catalão JPS (2009) Modeling and simulation of wind energy systems with matrix and multilevel power converters. IEEE Lat Am T 7: 78–84. doi: 10.1109/TLA.2009.5173468
[24]	William L (2010) The Control Handbook, 2nd ed., Florida: CRC Press.
[25]	Maciejowski JM, Goulart PJ, Kerrigan EC (2007) Constrained Control Using Model Predictive Control, In: Tarbouriech S, Garcia G, Glattfelder AH (eds), LNICS, Springer, Heidelberg, 346: 273–291.
[26]	Cassandras CG, Lafortune S (2000) Introduction to discrete event systems. Springer Science Business Media, New York, USA.
[27]	Viveiros C, Melicio R, Igreja JM, et al. (2015) Supervisory control of a variable speed wind turbine with doubly fed induction generator. Energ Rep 1: 89–95. doi: 10.1016/j.egyr.2015.03.001
[28]	Viveiros C, Melicio R, Igreja JM, et al. (2015) Performance assessment of a wind energy conversion system using a hierarchical controller structure. Energ Convers Manage 93: 40–48. doi: 10.1016/j.enconman.2015.01.002
[29]	Viveiros C, Melicio R, Igreja JM, et al. (2013) Application of a discrete adaptive LQG and fuzzy control design to a wind turbine benchmark model. In Proc. 2nd International Conference on Renewable Energy Research and Applications-ICRERA 2013, Madrid, Spain, 488–493.

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