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

被引：110

作者：

Poultangari, Iman ^{[1
]}

Shahnazi, Reza ^{[1
]}

Sheikhan, Mansour ^{[1
]}

机构：

[1] Islamic Azad Univ, S Tehran Branch, Dept Elect Engn, Tehran, Iran

来源：

ISA TRANSACTIONS | 2012年 / 51卷 / 05期

关键词：

Wind turbine; Pitch control; PI controller; Particle swarm optimization (PSO); Radial basis function (RBF) neural network; SPEED;

D O I：

10.1016/j.isatra.2012.06.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2012 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：641 / 648

页数：8

共 45 条

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