通过使用混合粒子群优化和遗传算法优化最佳的风力轮机配置来优化风电场布局.
Tarique Anwar Qureshi1, Vilas Warudkar2
1Department of Mechanical Engineering, M.A.N.I.T, Bhopal, India. taqmanit@gmail.com.
概括
一个新的混合算法 (HPSOGA) 优化了风电场布局,以获得最大的能源输出. 这种方法有效地平衡了粒子群优化和遗传算法,以克服风电场布局优化的复杂挑战.
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.
- 优化算法 优化算法
背景情况:
- 风力轮机 (WT) 的放置和配置显著影响风电场 (WF) 的性能和能源输出.
- 关键的设计考虑因素包括风速,风向和轮之间距离.
- 有效的风电场布局优化 (WFLO) 对于最大限度地提高发电量至关重要.
研究的目的:
- 提出一种新的混合算法,混合粒子群优化和遗传算法 (HPSOGA),用于优化风电场布局.
- 通过先进的优化来提高能源输出和风电场性能的一致性.
- 在WFLO问题中,应对多个局部优点所带来的挑战.
主要方法:
- 开发HPSOGA,将粒子群优化 (PSO) 结合为广泛的探索和基因算法 (GA) 为精细的解决方案.
- 一个两个阶段的框架,PSO优化初始参数,其次是GA驱动的参数调整以改善适应性.
- 使用Jenson-Wake模型进行唤醒效应分析,并将WT总成本和WF功率输出纳入目标函数.
主要成果:
- 在总输出发电量方面,HPSOGA与其他方法 (GA,BPSO-TVAC,L-SHADE,BRCGA,EO-PS) 相比表现优越.
- 该算法有效地处理多个局部最佳值的问题,从而导致更一致和更优化的布局.
- 模拟结果验证了HPSOGA在风电场布局优化方面的可靠性和有效性.
结论:
- HPSOGA是一种高效的混合算法,用于优化风电场布局.
- 与现有的算法相比,拟议的方法显著改善了总输出发电量.
- 该框架提供了一种可靠的方法来最大限度地提高风电场的效率和能源产量.
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