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

Ehsan Hosseini, Ehsan Aghadavoodi and Luis M. Fernández Ramírez

Renewable Energy, 2020, vol. 157, issue C, 897-910

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.

Keywords: Pith angle controller; Wind turbine; Gain-scheduled; ANFIS type-2 controller; Reinforcement learning (RL); Unsupervised clustering (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:157:y:2020:i:c:p:897-910

DOI: 10.1016/j.renene.2020.05.060

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