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具有随机选择性突变的新遗传灰狼优化器用于风电场布局优化

Mauro Amaro Pinazo¹

¹Department of Electrical Engineering, Faculty of Electrical and Electronic Engineering, National University of Engineering, Lima, Peru.

|December 17, 2024

概括

一个新的遗传灰狼优化器 (GGWO) 优化了风力轮机的位置,以提高能源生产. 这种方法有效地减少了唤醒效应,在模拟中表现优于其他算法.

科学领域:

可再生能源工程可再生能源工程
计算智能是一种计算智能.
空气动力学航空动力学

背景情况:

风电场的能源生产受到效应的显著影响,下游轮机的风速下降.
最佳的轮机位置对于最大限度地提高整体能源输出和最大限度地减少因唤醒干扰造成的损失至关重要.

研究的目的:

介绍一个新的优化算法,基因灰狼优化器 (GGWO),用于确定最佳的风力轮机分布.
通过减轻效应对能源发电的负面影响,提高风电场的效率.

主要方法:

拟议的遗传灰狼优化器 (GGWO) 将遗传算法运算符 (交叉,突变,随机选择性突变) 与层次狼群模型 (阿尔法,贝塔,三角形,欧米克朗) 集成在一起.
对GGWO的性能进行了评估,这些算法包括灰狼优化器 (GWO),粒子群优化 (PSO),人工蜂群 (ABC) 和群优化 (ACO).
模拟考虑了全年变化的风条件 (速度,方向) 和不同的风电场布局.

主要成果:

GGWO算法成功地确定了最佳风力轮机位置,从而改善了模拟持续时间和增加了年度能源发电量.
在进行的研究中,GGWO与GWO,ABC和PSO算法相比表现优越.
与更复杂的优化技术 (如ACO) 相比,该算法显示出具有竞争力的结果.

结论:

遗传灰狼优化器 (GGWO) 为优化风力轮机布局提供了一种有效的方法,以最大限度地提高能源生产.
GGWO的混合性质,将遗传运营商与社会层次模型相结合,提高了解决方案效率和搜索能力.
这种新的方法为提高风电场的经济可行性和运营效率提供了一个有希望的解决方案.

关键词:

年度能源生产量.遗传算法遗传算法遗传算法遗传灰狼优化器的遗传灰狼优化器随机选择性突变随机选择性突变唤醒效应唤醒效应风电场布局优化风电场布局优化

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