不同搜索空间下风电场布局优化:阿曼案例研究

A. Al Shereiqi, A. Al-Hinai, B. Mohandes, R. Al-Abri, Mohammed H. Albadi
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引用次数: 0

摘要

风电场布局对发电的影响是一个非常重要的课题。风电场布局优化是传统优化方法无法解决的复杂优化问题。因此,启发式优化技术被用于解决WFLO问题。对于这个问题,搜索空间在结果中起着重要的作用。因此,本研究探讨了使用遗传算法在连续和离散搜索空间中解决WFLO问题的影响。此外,本文还采用了Jensen尾迹效应模型来估计风电场内的速度亏损。以多速度、多方向的风廓线为例,演示了如何在离散和连续搜索空间中获得风电场布局。结果表明,连续搜索空间在紧凑性方面具有优势,而离散搜索空间具有更高的输出功率、效率和更短的计算时间。这些结果是由于遗传算法的代数、种群大小和获得最优性的计算机器的限制造成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind Farm Layout Optimization in Different Search Spaces: A Case Study in Oman
Wind farm layout is a vital subject due to its effect on power generation. Wind farm layout optimization (WFLO) is a complex optimization problem that cannot be solved by traditional optimization methods. Therefore, heuristic optimization techniques are used to solve a WFLO problem. With this problem, the search space plays a significant role in the results. Therefore, this study investigates the impacts of solving the WFLO problem in continuous and discrete search spaces using a genetic algorithm. Besides, Jensen's wake effect model is involved in this study to estimate the velocity deficit within the wind farm. A case study that features a wind profile with multi-speed and multi-direction is used to demonstrate how to get a wind farm layout in discrete and continuous search spaces. The results indicate for the superiority of a continuous search space in terms of compactness, whereas the discrete search space has higher output power, efficiency, and shorter computational time. These results are due to the limitations on the number of generations, population sizes, and the computational machine to get the optimality of the genetic algorithm.
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