对光伏系统最新的最大功率点跟踪技术进行全面研究
Mohammed Hamouda Ali1, Mohammad Zakaria2, Sally El-Tawab2
1Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo, 11651, Egypt. eng_mohammedhamouda@azhar.edu.eg.
Scientific reports
|April 24, 2025
概括
人工智能技术为太阳能光伏 (PV) 系统提供近99%的跟踪效率,优于传统和元启发方法. 这项研究指导了MPPT的选择,以实现最佳的光伏发电.
科学领域:
- 可再生能源系统可再生能源系统
- 光伏能源转换光伏能源转换
- 动力电子控制 电源电子控制
背景情况:
- 全球可再生能源正在增加,太阳能光伏 (PV) 由于可用性,安全性和环境效益导致产能扩张.
- 由于电池效率和环境变化的固有局限性,最大限度地提高光伏系统的能量产量至关重要.
- 最大功率点跟踪 (MPPT) 策略对于优化光伏系统输出功率至关重要.
研究的目的:
- 建模和开发先进的MPPT技术,以提高光伏系统的性能.
- 根据关键绩效指标,批判性地分析和比较各种MPPT方法.
- 通过使用多标准决策,为各种环境条件选择最佳MPPT方法提供指导方针.
主要方法:
- 研究了五种MPPT技术:传统 (Perturb & Observe,增量行为),元启发 (粒子群优化) 和人工智能 (人工神经网络,模糊逻辑控制).
- 基于跟踪速度,算法复杂性和在不同的环境条件下动态跟踪的评估技术.
- 雇员多标准决策 (MCDM) 使用 AHP,CRITIC 和 VIKOR 进行比较分析和基于有效性和经济可行性的排名.
主要成果:
- 人工智能 (AI) 技术显示出卓越的跟踪效率,达到大约99%.
- 与传统和元启发式方法相比,人工智能方法表现出更快,更有效的跟踪速度.
- 该研究提供了MPPT方法的排名比较,突出了AI在性能和速度方面的优势.
结论:
- 基于人工智能的MPPT技术对于最大限度地利用太阳能光伏发电非常有效.
- 综合分析和MCDM方法为选择适当的MPPT策略提供了宝贵的见解.
- 这项研究是优化光伏系统运行和经济可行性的关键参考.
关键词:
人工智能 (AI) 是一种人工智能.增量导电性 (INC) 是指增量导电性.在MCDM中,MCDM是MCDM.在MPPTT中,MPPT是MPPT,MPPT是MPPT.太阳能系统的光伏系统.粒子群集优化 (PSO) 是一种干扰和观察 (P&O) 的情况更多相关视频
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