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An Adaptive Model-Based Mutation Operator for the Wind Farm Layout Optimisation Problem

机译:风电场布局优化问题的基于模型的自适应变异算子

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A novel mutation operator for the wind farm layout optimisation problem is proposed and tested. When a wind farm layout is simulated, statistics such as an individual turbine's wake free ratio can be computed. These statistics are in addition to the global measure being optimised, for example the overall cost of energy extraction of the farm. We present algorithms that first of all build a predictive model of the wake free ratio across an entire wind farm. This model is then used inside a mutation operator to perturb turbines towards positions of high predicted wake free ratio. We evaluate our approach by comparing a 1+1 Evolutionary Strategy using this new mutation operator vs. The same algorithm with a more standard random mutation operator, and show that our new operator leads to the discovery of wind farm layouts having a statistically significantly lower cost of energy extraction.
机译:提出并测试了一种针对风电场布局优化问题的新型变异算子。模拟风电场布局时,可以计算统计数据,例如单个涡轮的无尾流比率。这些统计信息是对全球指标进行优化后的补充,例如农场能源提取的总成本。我们提出的算法首先建立了整个风电场的无尾流比率的预测模型。然后,该模型在变异算子内部使用,以使涡轮机趋向于具有较高预测自由度的位置。我们通过比较使用此新变异算子的1 + 1进化策略与具有更标准随机变异算子的相同算法来评估我们的方法,并表明我们的新算子导致发现具有统计学意义上更低成本的风电场布局能量提取。

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