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

Iman Poultangari; Reza Shahnazi; Mansour Sheikhan

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

机译：基于粒子群优化算法的一类5兆瓦风力发电机组基于RBF神经网络的PI变桨控制器

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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.

机译：为了控制风力涡轮机中叶片的桨距角，通常使用比例和积分（PI）控制器，因为它具有简单性和工业实用性。神经网络和进化算法是为确定最佳PI增益提供合适基础的工具。本文提出了一种基于径向基函数（RBF）神经网络的PI控制器，用于5MW风力发电机组的集中变桨控制（CPC）。为了提供训练RBF神经网络的最佳数据集，使用了粒子群优化（PSO）进化算法。该方法不需要控制系统的复杂性，非线性和不确定性。仿真结果表明，该控制器具有令人满意的性能。

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来源
《ISA Transactions》 |2012年第5期|共8页
作者
Iman Poultangari; Reza Shahnazi; Mansour Sheikhan;
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正文语种 eng
中图分类计量学;
关键词
Wind turbine; Pitch control; PI controller; Particle swarm optimization (PSO); Radial basis function (RBF) neural network;

机译：风力发电机;变桨控制;PI控制器;粒子群优化（PSO）;径向基函数（RBF）神经网络;

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1. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm [J] . Iman Poultangari, Reza Shahnazi, Mansour Sheikhan ISA Transactions . 2012,第5期

机译：基于粒子群优化算法的一类5兆瓦风力发电机组基于RBF神经网络的PI变桨控制器
2. A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm [J] . Assareh Ehsanolah, Biglari Mojtaba Renewable & Sustainable Energy Reviews . 2015,第PTa899a1798期

机译：利用PI控制器，RBF神经网络和GSA进化算法从变速风力涡轮机捕获最大功率的新方法
3. A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm [J] . Assareh Ehsanolah, Biglari Mojtaba Renewable & Sustainable Energy Reviews . 2015,第NOVa期

机译：利用PI控制器，RBF神经网络和GSA进化算法从变速风力涡轮机捕获最大功率的新方法
4. Analysis of a RBF Neural Network Based Controller for Pitch Angle of Variable-Speed Wind Turbines [C] . El Kabira El Mjabber, Abdellah El Hajjaji, Abdellatif Khamlichi International Conference Interdisciplinarity in Engineering . 2017

机译：用于变速风力涡轮机的俯仰角的RBF神经网络基于RBF神经网络的分析
5. Controller design for a tri-turbofan airship system based on particle swarm optimization algorithm & neural network technology. [D] . Jia, Ruting. 2008

机译：基于粒子群算法和神经网络技术的三涡扇飞艇系统控制器设计。
6. Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms [O] . Xinyong Zhang, Liwei Sun 2021

机译：基于粒子群优化和贝叶斯正则化算法的反向化神经网络优化光学机械结构
7. Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller [O] . A. Asgharnia, A. Jamali, R. Shahnazi, 2020

机译：RBF基于RBF神经网络的分数阶PID控制器的一类5 MW风力涡轮机的负载减缓

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

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