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Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning

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  • Hosseini, Ehsan
  • Aghadavoodi, Ehsan
  • Fernández Ramírez, Luis M.

Abstract

In this paper, passive reinforcement learning (RL) solved by particle swarm optimization policy (PSO–P) is used to handle an adaptive neuro-fuzzy inference system (ANFIS) type-2 structure with unsupervised clustering for controlling the pitch angle of a real wind turbine (WT). The proposed control scheme is based on gain-scheduled reinforcement learning recurrent ANFIS type 2 (GS-RL-RANFIST2) pitch angle controller to maintain the rotor speed at its rated value while smoothing the output power and the performance of the pitch angle system. The practical application of the proposed controller is evaluated by using FAST tool for a real 600 kW WT equipped with a synchronous generator with a full-size power converter (CART3, located at the National Renewable Energy Laboratory, NREL), whose results are compared with those obtained by a gain corrected proportional integral (GC-PI) controller. The results demonstrate that the GS-RL-RANFIST2, which sets the nonlinear characteristics of the system automatically and waves more uncertainties in the windy conditions, allows to increase the energy capture and smooth the output power fluctuation, and therefore, to improve the control and response of the WT.

Suggested Citation

  • Hosseini, Ehsan & Aghadavoodi, Ehsan & Fernández Ramírez, Luis M., 2020. "Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning," Renewable Energy, Elsevier, vol. 157(C), pages 897-910.
    • Handle: RePEc:eee:renene:v:157:y:2020:i:c:p:897-910
    DOI: 10.1016/j.renene.2020.05.060
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    References listed on IDEAS

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    1. Fernandez, L.M. & Garcia, C.A. & Jurado, F., 2010. "Operating capability as a PQ/PV node of a direct-drive wind turbine based on a permanent magnet synchronous generator," Renewable Energy, Elsevier, vol. 35(6), pages 1308-1318.
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    3. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
    4. Abdalrahman, Gebreel & Melek, William & Lien, Fue-Sang, 2017. "Pitch angle control for a small-scale Darrieus vertical axis wind turbine with straight blades (H-Type VAWT)," Renewable Energy, Elsevier, vol. 114(PB), pages 1353-1362.
    5. Daniel Hein & Alexander Hentschel & Thomas A. Runkler & Steffen Udluft, 2016. "Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 7(3), pages 23-42, July.
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    Cited by:

    1. Padullaparthi, Venkata Ramakrishna & Nagarathinam, Srinarayana & Vasan, Arunchandar & Menon, Vishnu & Sudarsanam, Depak, 2022. "FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning," Renewable Energy, Elsevier, vol. 181(C), pages 445-456.

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