通过使用PSO和GA技术优化模糊逻辑控制来增强与电网连接的风能转换系统.
Abdelhalim Borni1, Noureddine Bessous2, Layachi Zaghba1
1Centre de Développement des Energies Renouvelables, Unité de Recherche Appliquée en Energies Renouvelables, URAER, CDER, Ghardaïa, 47133, Algeria.
Scientific reports
|July 29, 2025
概括
这项研究优化了使用粒子群优化 (PSO) 和遗传算法 (GA) 的风力轮机的模糊逻辑控制器. 带有PSO的优化模糊逻辑控制器 (FLC) 在最大限度地提高功率输出和系统稳定性方面表现出卓越的性能.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 控制系统 控制系统
背景情况:
- 风能转换系统 (WECS) 面临着风速变化和电网波动的挑战.
- 有效的最大功率点跟踪 (MPPT) 对于最大限度地提高风力轮机的能量产量至关重要.
- 像扰动和观察 (P&O) 这样的传统MPPT方法在动态条件下有局限性.
研究的目的:
- 为网联风力轮机设计和模拟一个优化的模糊逻辑MPPT控制器.
- 为了比较优化模糊逻辑控制器 (FLC) 与传统方法的性能.
- 评估粒子优化 (PSO) 和遗传算法 (GA) 在提高FLC性能方面的有效性.
主要方法:
- 实施了四种MPPT方法:P&O,独立的FLC,FLC-GA和FLC-PSO.
- 利用PSO和GA优化FLC的输入和输出会员功能.
- 进行模拟以分析不同风条件下的系统性能.
主要成果:
- 与FLC-GA和P&O相比,FLC-PSO方法显示了更快的收 (过渡时间为0.05秒) 和更高的跟踪精度.
- 优化的FLC有效地缓解了扭曲和风速突然变化等问题.
- 与FLC-GA相比,FLC-PSO提供了更高的效率,稳定性和计算简单性.
结论:
- 优化的模糊逻辑控制器,特别是FLC-PSO,显著提高WECS性能.
- 由于其速度和准确性,PSO是FLC-MPPT的首选优化技术.
- 拟议的FLC-PSO方法代表了可再生能源整合和电网管理的进步.
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