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RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm



RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm



Isa Transactions 51(5): 641-648



In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.

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Accession: 055333632

Download citation: RISBibTeXText

PMID: 22738782

DOI: 10.1016/j.isatra.2012.06.001



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