Use of Adaptive Linear Algorithms for Very Short-Term Prediction of Wind Turbine Power Output

被引：0

作者：

Tohidian, Mahdi ^{[1
]}

Esmaili, A. ^{[2
]}

Naghizadeh, Ramezan-Ali ^{[3
]}

Sadeghi, S. H. H. ^{[1
,3
]}

Nasiri, A. ^{[3
]}

Reza, Ali M. ^{[3
]}

机构：

[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran

[2] Hamedan Univ Technol, Dept Elect & Comp Engn, Hamadan, Iran

[3] Univ Wisconsin Milwaukee, Dept Elect Engn & Comp Sci, Milwaukee, WI 53706 USA

来源：

38TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2012) | 2012年

关键词：

SPEED PREDICTION; NEURAL-NETWORKS; OAXACA;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The paper proposes an efficient method for very short-term prediction of wind turbine power output. The method, which models the turbine as a Hammerstein system, exploits an adaptive linear filtering algorithm. The performance of the proposed method is examined by implementation of two linear adaptive algorithms, namely, least mean squares (LMS) and recursive least squares (RLS) filters. Using synthetic generation of turbine power output, it is shown that the RLS algorithm gives more accurate results with moderate computational burden as compared to the LMS algorithm and rival artificial neural networks.

引用

页码：1162 / 1165

页数：4

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