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

[21] Self-adapting PI controller for grid-connected DFIG wind turbines based on recurrent neural network optimization control under unbalanced grid faults
Chetouani, Elmostafa
Errami, Youssef
Obbadi, Abdellatif
Sahnoun, Smail
ELECTRIC POWER SYSTEMS RESEARCH, 2023, 214
[22] Self-adapting PI controller for grid-connected DFIG wind turbines based on recurrent neural network optimization control under unbalanced grid faults
Chetouani, Elmostafa
Errami, Youssef
Obbadi, Abdellatif
Sahnoun, Smail
ELECTRIC POWER SYSTEMS RESEARCH, 2023, 214
[23] PSC Ship-Selecting Model Based on Improved Particle Swarm Optimization and BP Neural Network Algorithm
Yang, Tingting
Sun, Zhonghua
Wang, Shouna
Yang, Chengming
Lin, Bin
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2014, PT II, 2014, 8631 : 425 - 435
[24] A Novel Pattern Clustering Algorithm Based on Particle Swarm Optimization Joint Adaptive Wavelet Neural Network Model
Shoubao Su
Haifeng Guo
Haimei Tian
Gang Wu
Mobile Networks and Applications, 2017, 22 : 692 - 701
[25] Convolutional Neural Network Architecture Design Using an Improved Surrogate-Assisted Particle Swarm Optimization Algorithm
Zhao, Xin
Qi, Jiajing
Cao, Yahui
Zhang, Tao
Geng, Yanzhang
Wang, Yang
ADVANCES IN SWARM INTELLIGENCE, PT I, ICSI 2024, 2024, 14788 : 85 - 96
[26] A Novel Pattern Clustering Algorithm Based on Particle Swarm Optimization Joint Adaptive Wavelet Neural Network Model
Su, Shoubao
Guo, Haifeng
Tian, Haimei
Wu, Gang
MOBILE NETWORKS & APPLICATIONS, 2017, 22 (04) : 692 - 701
[27] Optimal operation of induction motors using artificial neural network based on particle swarm optimization (PSO)
Hamid, Radwan H. A.
Amin, Amr M. A.
Ahmed, Refaat S.
El-Gammal, Adel A. A.
2006 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY, VOLS 1-6, 2006, : 68 - +
[28] Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network
Roy, Pratik
Mahapatra, Ghanshaym Singha
Dey, Kashi Nath
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2019, 6 (06) : 1365 - 1383
[29] Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network
Pratik Roy
Ghanshaym Singha Mahapatra
Kashi Nath Dey
IEEE/CAA Journal of Automatica Sinica, 2019, 6 (06) : 1365 - 1383
[30] Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization
Yang, Xiyun
Zhang, Yanfeng
Yang, Yuwei
Lv, Wei
APPLIED SCIENCES-BASEL, 2019, 9 (09):

← 1 2 3 4 5 →