Int. J. of Intelligent Engineering Informatics   »   2016 Vol.4, No.3/4

 

 

Title: Optimal intelligent control for a variable-speed wind turbine using general regression neural network and adaptive PSO algorithm

 

Authors: El-Mahjoub Boufounas; Miloud Koumir; Ismail Boumhidi

 

Addresses:
LESSI Laboratory, Department of Physics, Faculty of Sciences Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P. 1796, 30000 FES-Atlas, Morocco
LESSI Laboratory, Department of Physics, Faculty of Sciences Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P. 1796, 30000 FES-Atlas, Morocco
LESSI Laboratory, Department of Physics, Faculty of Sciences Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, B.P. 1796, 30000 FES-Atlas, Morocco

 

Abstract: In this paper, a robust optimal general regression neural network sliding mode (GRNNSM) controller is designed for a variable speed wind turbine (VSWT). The main objective of the controller is to optimise the energy captured from the wind. Sliding mode control (SMC) approach emerges as an especially suitable option to deal with a VSWT. However, for large uncertain systems, the SMC produces chattering problems due to the higher needed switching gain. In order to guarantee the wind power capture optimisation without any chattering problems, this study propose to combine the SMC with the general regression neural network (GRNN) based on adaptive particle swarm optimisation (APSO) algorithm. The GRNN is used for the prediction of uncertain model part and hence enable a lower switching gain to be used for compensating only the prediction errors. The APSO algorithm with efficient global search is used to train the weights of GRNN in order to improve the network performance in terms of the speed of convergence and error level. The stability is shown by the Lyapunov theory and the control action used did not exhibit any chattering behaviour. The effectiveness of the designed method is illustrated in simulations by the comparison with traditional SMC.

 

Keywords: variable speed wind turbines; VSWT; sliding mode control; SMC; general regression neural networks; GRNN; adaptive PSO; particle swarm optimisation; APSO; optimal control; intelligent control; controller design; wind energy; wind power; simulation.

 

DOI: 10.1504/IJIEI.2016.10001298

 

Int. J. of Intelligent Engineering Informatics, 2016 Vol.4, No.3/4, pp.267 - 285

 

Submission date: 26 Sep 2015
Date of acceptance: 11 Apr 2016
Available online: 16 Nov 2016

 

 

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