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Research Papers

A Multi-Objective Optimization Approach to Active Power Control of Wind Farm

[+] Author and Article Information
Jianxiao Zou

School of Automation Engineering,
University of Electronic Science
and Technology of China,
Chengdu 611731, China
e-mail: jxzou@uestc.edu.cn

Junping Yao

School of Automation Engineering,
University of Electronic Science
and Technology of China,
Chengdu 611731, China
e-mail: yaojunping16@gmail.com

Qingze Zou

Department of Mechanical and
Aerospace Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: qzzou@rci.rutgers.edu

Hongbing Xu

School of Automation Engineering,
University of Electronic Science
and Technology of China,
Chengdu 611731, China
e-mail: hbxu@uestc.edu.cn

Zhenzhen Zhang

College of Electrical and
Information Engineering,
Southwest University for Nationalities,
Chengdu 611731, China
e-mail: zhangzhenzhen.isit@gmail.com

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received November 16, 2012; final manuscript received November 16, 2013; published online February 20, 2014. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 136(2), 021026 (Feb 20, 2014) (8 pages) Paper No: SOL-12-1313; doi: 10.1115/1.4026636 History: Received November 16, 2012; Revised November 16, 2013; Accepted December 02, 2013

With more and more wind farms integrated into the power grid, the stability and security of the grid can be significantly affected by the wind-farm-generated power, due to the intermittent and volatile nature of the wind-farm-generated power. Therefore, control of the wind-farm power to meet the stability and quality requirements becomes important. Active control of wind-farm power, however, is challenging because the wind-farm output power can only be reliably predicted for a short period of time (i.e., ultrashort term power prediction), and large variations exist in the wind-turbine output power. In this paper, an optimal active power control scheme is proposed to maximize the running time of each wind turbine, and minimize the on-and/or-off switching of wind turbines, resulting in substantial reduction of wind-turbine wear and thereby, maintenance cost, and extension of wind-turbine lifetime, all together, a significant saving of operation cost of the whole wind farm. The proposed approach is illustrated by implementing it to the active power allocation of a wind-farm model in simulation.

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Figures

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Fig. 1

Active power control for wind farm

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Fig. 2

Multiparameter cascade binary encoding

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Fig. 3

Comparison of the actual output power and the predicted value obtained by using the proposed ultrashort-term power prediction method

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Fig. 4

(a) Comparison of the wind-farm output obtained by using the proposed APC technique to the desired one, and the wind-farm output obtained by using the average algorithm. (b) Comparison of the active power tracking error of wind farm obtained by using the proposed APC technique to the desired one, and the active power tracking error of wind farm obtained by using the average algorithm.

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