期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 3, 页码 1040-1054出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2743761
关键词
Differential evolution (DE); encoding mechanism; optimization; wake effect; wind farm layout
类别
资金
- National Natural Science Foundation of China [61673397, 61673331]
- EU [661327]
- Engineering and Physical Sciences Research Council of UK [EP/K001310/1]
- Hunan Provincial Natural Science Fund for Distinguished Young Scholars [2016JJ1018]
- EPSRC [EP/K001310/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K001310/1] Funding Source: researchfish
- Marie Curie Actions (MSCA) [661327] Funding Source: Marie Curie Actions (MSCA)
This paper presents a differential evolution algorithm with a new encoding mechanism for efficiently solving the optimal layout of the wind farm, with the aim of maximizing the power output. In the modeling of the wind farm, the wake effects among different wind turbines are considered and the Weibull distribution is employed to estimate the wind speed distribution. In the process of evolution, a new encoding mechanism for the locations of wind turbines is designed based on the characteristics of the wind farm layout. This encoding mechanism is the first attempt to treat the location of eachwind turbine as an individual. As a result, the whole population represents a layout. Compared with the traditional encoding, the advantages of this encoding mechanism are twofold: 1) the dimension of the search space is reduced to two, and 2) a crucial parameter (i.e., the population size) is eliminated. In addition, differential evolution serves as the search engine and the caching technique is adopted to enhance the computational efficiency. The comparative analysis between the proposed method and seven other state-of-the-art methods is conducted based on two wind scenarios. The experimental results indicate that the proposed method is able to obtain the best overall performance, in terms of the power output and execution time.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据