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

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.

► This paper proposes a novel pitch controller for a class of 5 MW wind turbines. ► The controller is an optimal PI controller. ► The controller is based on RBF neural network and PSO algorithm. ► The proposed controller adapts itself to any wind speed profile. ► Simulation results show the effectiveness of the proposed controller.