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

[1] MPPT control of wind generation systems based on estimated wind speed using SVR [J].

Abo-Khalil, Ahmed G. ;

Lee, Dong-Choon .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2008, 55 (03) :1489-1490

[2] Help-Training for semi-supervised support vector machines [J].

Adankon, Mathias M. ;

Cheriet, Mohamed .

PATTERN RECOGNITION, 2011, 44 (09) :2220-2230

[3]

An Y, 2005, J WIND ENG IND AEROD, V93, P535, DOI [10.1016/j.jweia.2005.05.003, 10.1016/j.jwei.2005.05.003]

[4] Batch-mode semi-supervised active learning for statistical machine translation [J].

Ananthakrishnan, Sankaranarayanan ;

Prasad, Rohit ;

Stallard, David ;

Natarajan, Prem .

COMPUTER SPEECH AND LANGUAGE, 2013, 27 (02) :397-406

[5]

[Anonymous], 2012, INT J ELECT COMPUT S

[6]

[Anonymous], 2008, P INT S ADV BOUND LA, DOI DOI 10.1088/1755-1307/1/1/012066

[7] An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines [J].

Ata, R. ;

Kocyigit, Y. .

EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (07) :5454-5460

[8]

Awad M., 2015, EFFICIENT LEARNING M, P67, DOI [10.1007/978-1-4302-5990-9_4, DOI 10.1007/978-1-4302-5990-9_4]

[9] Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis [J].

Balahur, Alexandra ;

Turchi, Marco .

COMPUTER SPEECH AND LANGUAGE, 2014, 28 (01) :56-75

[10] Support vector regression for link load prediction [J].

Bermolen, Paola ;

Rossi, Dario .

COMPUTER NETWORKS, 2009, 53 (02) :191-201

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