[720]波能转换器数据集（Wave Energy Converters Data Set）
0、数据编号：720
1、数据名称：波能转换器数据集（Wave Energy Converters Data Set）
2、数据来源：Adelaide University
3、时间跨度：截至2019-06-30
4、区域范围：
5、数据大小：55.9MB
6、数据格式：csv
7、数据简介：该数据集由澳大利亚南部海岸四种真实波浪情景中波浪能转换器（WEC）的位置和吸收功率输出组成。该数据集包括来自澳大利亚南部海岸（悉尼、阿德莱德、珀斯和塔斯马尼亚）的四个真实波浪情景中波浪能转换器 （WEC） 的位置和吸收功率输出。应用的转换器模型是称为CETO [1]的全浸没式三系线转换器。16个WEC位置在大小受限的环境中放置和优化。在优化方面，该问题被归类为一个昂贵的优化问题，每个服务器场评估需要几分钟。结果来自 [2，3] 中发表的几种流行且成功的进化优化方法。
属性信息：
属性：属性范围
1。WEC 位置 {X1， X2， ̦， X16;Y1， Y2，Y16} 从 0 到 566 （m） 连续。
2. WEC吸收功率： {P1， P2， …， P16}
3.农场总功率输出：功率全部
剂量（10^8/kg）英文原文：
Coronavirus Disease (COVID-19) Surveillance.
Attribute Information:
Symptoms of COVID-19
英文原文：This data set consists of positions and absorbed power outputs of wave energy converters (WECs) in four real wave scenarios from the southern coast of Australia. This data set consists of positions and absorbed power outputs of wave energy converters (WECs) in four real wave scenarios from the southern coast of Australia (Sydney, Adelaide, Perth and Tasmania). The applied converter model is a fully submerged three-tether converter called CETO [1]. 16 WECs locations are placed and optimized in a size-constrained environment. In terms of optimization, the problem is categorised as an expensive optimization problem that each farm evaluation takes several minutes. The results are derived from several popular and successful Evolutionary optimization methods that are published in [2,3].
Attribute Information:
Attribute: Attribute Range
1. WECs position {X1, X2, …, X16; Y1, Y2,…, Y16} continuous from 0 to 566 (m).
2. WECs absorbed power: {P1, P2, …, P16}
3. Total power output of the farm: Powerall
参考文献：
[1] L. D. Mann, A. R. Burns, , and M. E. Ottaviano. 2007. CETO, a carbon free wave power energy provider of the future. In the 7th European Wave and Tidal Energy Conference (EWTEC).
[2] Neshat, M., Alexander, B., Wagner, M., & Xia, Y. (2018, July). A detailed comparison of meta-heuristic methods for optimising wave energy converter placements. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1318-1325). ACM.
[3] Neshat, M., Alexander, B., Sergiienko, N., & Wagner, M. (2019). A new insight into the Position Optimization of Wave Energy Converters by a Hybrid Local Search. arXivpreprint
