Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study

被引：6

作者：

Petkovic, Dalibor ^{[1
]}

Arif, Muhammad ^{[2
]}

Shamshirband, Shahaboddin ^{[3
]}

Bani-Hani, Ehab Hussein ^{[4
]}

Kiakojoori, Davood ^{[5
]}

机构：

[1] Univ Nis, Fac Mech Engn, Dept Mechatron & Control, Nish 18000, Serbia

[2] Gabriel Coll Mandi Bahauddin, Dept Comp Sci, Mandi Bahauddin, Pakistan

[3] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur, Malaysia

[4] Australian Coll Kuwait, Dept Mech Engn, Sch Engn, Kuwait, Kuwait

[5] Islamic Azad Univ Chalus, Mazandaran, Iran

来源：

INFORMATICA | 2015年 / 26卷 / 03期

关键词：

wind pace; wind turbine; ANFIS; support vector regression; soft computing; SUPPORT VECTOR REGRESSION; FUZZY INFERENCE SYSTEM; MACHINE; TURBINE; PREDICTION; ALGORITHM; QUALITY; MODEL; DIRECTION; CAPTURE;

D O I：

10.15388/Informatica.2015.60

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results.

引用

页码：493 / 508

页数：16

共 70 条

[51] Support vector regression methodology for storm surge predictions [J].

Rajasekaran, S. ;

Gayathri, S. ;

Lee, T. -L. .

OCEAN ENGINEERING, 2008, 35 (16) :1578-1587

[52] Application of neural network controller for maximum power extraction of a grid-connected wind turbine system [J].

Ro, K ;

Choi, HH .

ELECTRICAL ENGINEERING, 2005, 88 (01) :45-53

[53] A sensorless control for a variable speed wind turbine operating at partial load [J].

Rocha, Ronilson .

RENEWABLE ENERGY, 2011, 36 (01) :132-141

[54]

Sedighizadeh M, 2008, P WORLD ACAD SCI ENG, V27, P1307

[55] Variable speed control of wind turbines using nonlinear and adaptive algorithms [J].

Song, YD ;

Dhinakaran, B ;

Bao, XY .

JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2000, 85 (03) :293-308

[56] Parameter estimation of surface layer turbulence from wind speed vertical profile [J].

Sozzi, R ;

Rossi, F ;

Georgiadis, T .

ENVIRONMENTAL MODELLING & SOFTWARE, 2001, 16 (01) :73-85

[57] Simultaneous measurements of wind speed profiles at two sites using Doppler sodars [J].

Tamura, Y ;

Suda, K ;

Sasaki, A ;

Iwatani, Y ;

Fujii, K ;

Ishibashi, R ;

Hibi, K .

JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2001, 89 (3-4) :325-335

[58] Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization [J].

Tang, Xianlun ;

Zhuang, Ling ;

Jiang, Changjiang .

EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (09) :11853-11857

[59] A Gaussian RBF Network Based Wind Speed Estimation Algorithm for Maximum Power Point Tracking [J].

Tian, Lei ;

Lu, Qiang ;

Wang, Wen-zhuo .

PROCEEDINGS OF INTERNATIONAL CONFERENCE ON SMART GRID AND CLEAN ENERGY TECHNOLOGIES (ICSGCE 2011), 2011, 12

[60] Analysis of some flexible families of distributions for estimation of wind speed distributions [J].

Usta, Ilhan ;

Kantar, Yeliz Mert .

APPLIED ENERGY, 2012, 89 (01) :355-367

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