最佳的最大功率点跟踪策略基于大鼠算法,用于风能转换系统.
Kareem M AboRas1, Mohammed Hassan El-Banna2, Ashraf Ibrahim Megahed2
1Electrical Power and Machines Department, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt. kareem.aboras@alexu.edu.eg.
Scientific reports
|September 12, 2025
概括
本研究介绍了大鼠算法 (GCRA) 用于优化风能转换系统 (WECS). 与其他方法相比,GCRA显著提高了最大功率点跟踪 (MPPT) 的效率,超过99%.
科学领域:
- 可再生能源系统可再生能源系统
- 电气工程 电气工程
- 计算智能是一种计算智能.
背景情况:
- 来自风能转换系统 (WECS) 的最大功率输出对于增加可再生能源采用至关重要.
- 传统的方法,如Perturb和观察 (P&O) 最大功率点跟踪 (MPPT) 由于波动的风条件而遭受不准确.
- 智能优化技术对于WECS中有效的MPPT是必要的.
研究的目的:
- 介绍和评估大鼠算法 (GCRA),一个新的自然灵感的元启发式,为MPPT在WECS.
- 将GCRA的性能与P&O,粒子群优化 (PSO) 和灰狼优化 (GWO) 等既有方法进行比较.
- 为了证明GCRA在调节增压转换器以获得最佳功率提取而没有机械传感器方面的能力.
主要方法:
- 这项研究使用了Greater Cane Rat Algorithm (GCRA),模拟食行为来计算DC/DC增压转换器的工作周期.
- 该WECS模型包括一个风力轮机,永磁同步发电机 (PMSG),整流器和一个负载,在MATLAB/SIMULINK中实现.
- 性能根据各种风速配置文件进行评估:步骤,现实和坡道变化.
主要成果:
- 拟议的GCRA战略实现了超过99%的跟踪效率,超过了P&O (95.5%),PSO (94.7%) 和GWO (91.4%).
- 在最小的误差比率和功率系数比率的最佳跟踪方面,GCRA表现出卓越的性能.
- 该系统成功执行了无传感器的最大功率跟踪,简化了WECS设计.
结论:
- 大鼠算法 (GCRA) 为风能转换系统的最大功率点跟踪提供了高效和高效的解决方案.
- GCRA为传统的MPPT方法提供了强大而准确的替代方案,特别是在动态的风力条件下.
- GCRA的无传感器跟踪能力提高了WECS的实用性和性能.
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