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Collection line optimisation in wind farms using improved ant colony optimisation

Collection line optimisation in wind farms using improved ant colony optimisation Collection line optimisation plays an important role in reducing the wind farm construction cost and improving the reliability of collector system. First, the optimisation model of collection lines in wind farms is given. Based on the four-vertex-three-line inequality and Prim algorithm, an improved ant colony optimisation algorithm is introduced to minimise the total length of collection lines to reduce their cost. In addition, the K-means clustering algorithm is adopted to partition the total wind turbines into several groups for the improved ant colony optimisation algorithm to search the shortest collection lines. The improved ant colony optimisation algorithm is tested with a wind farm example and compared with the classic ant colony optimisation algorithm. Under the same preconditions, the experimental results show that compared to the classic ant colony optimisation algorithm, the improved ant colony optimisation algorithm finds shorter collection lines in radial configuration for the wind farm. In addition, the improved ant colony optimisation algorithm requires less computation cycles to find better collection lines. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wind Engineering SAGE

Collection line optimisation in wind farms using improved ant colony optimisation

Wu, Yi-wen; Wang, Yong
Wind Engineering , Volume 45 (3): 12 – Jun 1, 2021
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References (18)

Publisher
SAGE
Copyright
© The Author(s) 2020
ISSN
0309-524X
eISSN
2048-402X
DOI
10.1177/0309524x20917319
Publisher site
See Article on Publisher Site

Abstract

Collection line optimisation plays an important role in reducing the wind farm construction cost and improving the reliability of collector system. First, the optimisation model of collection lines in wind farms is given. Based on the four-vertex-three-line inequality and Prim algorithm, an improved ant colony optimisation algorithm is introduced to minimise the total length of collection lines to reduce their cost. In addition, the K-means clustering algorithm is adopted to partition the total wind turbines into several groups for the improved ant colony optimisation algorithm to search the shortest collection lines. The improved ant colony optimisation algorithm is tested with a wind farm example and compared with the classic ant colony optimisation algorithm. Under the same preconditions, the experimental results show that compared to the classic ant colony optimisation algorithm, the improved ant colony optimisation algorithm finds shorter collection lines in radial configuration for the wind farm. In addition, the improved ant colony optimisation algorithm requires less computation cycles to find better collection lines.

Journal

Wind Engineering – SAGE

Published: Jun 1, 2021

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