多忠度贝叶斯式优化风电场警觉方向使用警觉模型和大模拟.
1Department of Aeronautics, Imperial College London, London, SW7 2AZ UK.
概括
这项研究表明,像大模拟 (LES) 这样的高保真模拟比分析模型更好地优化风力发电场偏航配置. 多忠实度贝叶斯优化框架降低了用于改善风电场发电量的计算成本.
科学领域:
- 可再生能源工程可再生能源工程
- 计算流体动力学的流体动力学.
- 空气动力学 航空动力学
背景情况:
- 风力轮机清醒降低了效率,增加了风力发电场的疲劳.
- 警觉转向策略可以提高风电场的总发电量.
- 分析性唤醒模型是常用的,但有局限性.
研究的目的:
- 为了比较分析唤醒模型和高准确度大模拟 (LES) 的有效性,以优化风电场曲面配置.
- 引入一个多忠度贝叶斯优化框架,以降低与LES相关的计算成本.
主要方法:
- 利用大模拟 (LES) 来进行更高可靠性的风电场流量场建模.
- 开发了一个多忠实度贝叶斯优化框架,使用代用模型.
- 采用多忠度获取函数来指导优化代.
主要成果:
- 与分析模型相比,大型旋转模拟确定了与分析模型相比,对于完整的风电场来说,更优的转向配置.
- 多忠度贝叶斯优化框架实现了与单忠度优化相似的结果,成本更低的LES运行.
- 显著降低计算成本被证明可以实现最佳的风电场电力改进.
结论:
- 高保真度的 LES 更有效地捕捉复杂的唤醒相互作用,以优化曲折.
- 拟议的多忠度贝叶斯优化框架为风电场优化提供了一种计算效率高的方法.
- 这种方法可以以较低的计算成本实现大幅度的风电场电力增长.
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