Title: Optimal H control without reaching phase for a variable speed wind turbine based on fuzzy neural network and APSO algorithm

Authors: El-mahjoub Boufounas; Jaouad Boumhidi; Ismail Boumhidi

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

Abstract: This paper presents a novel optimal H tracking-based adaptive fuzzy neural network controller (HAFNNC) for a variable speed wind turbine. The main objective of the controller is to optimise the energy captured from the wind. In the presence of large uncertainties, H control approach produces oscillatory phenomenon due to the higher needed gain. In order to reduce this gain, fuzzy neural network (FNN) with online adaptation of the parameters is used to estimate the uncertain parts of the system plant and hence enable a lower gain to be used. To eliminate the trade-offs between the H tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of both the exponential function and the adaptive particle swarm optimisation (APSO) algorithm. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of the proposed approach.

Keywords: variable speed wind turbines; fuzzy neural networks; FNNs; H-infinity control; reaching phase; adaptive PSO; particle swarm optimisation; APSO; optimal control; controller design; wind energy; wind power; output tracking error; simulation.

DOI: 10.1504/IJMIC.2015.071891

International Journal of Modelling, Identification and Control, 2015 Vol.24 No.2, pp.100 - 109

Received: 22 Jul 2014
Accepted: 17 Mar 2015

Published online: 22 Sep 2015 *

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