一种改进的基于虫的MPPT方法,以减少光伏系统在部分遮阳条件下的跟踪时间和启动振荡
Muhammad Shahid Wasim1, Muhammad Amjad1, Muhammad Abbas Abbasi1
1Faculty of Engineering, The Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.
PloS one
|August 25, 2023
概括
一个改进的优化算法 (IGOA) 提高了光伏 (PV) 系统在部分阴影下的性能. 这种新方法以最小的振荡实现更快的全球最大功率点 (GMPP) 追踪,改善能量收集.
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
- 优化算法 优化算法
背景情况:
- 光伏 (PV) 系统在全球最大功率点 (GMPP) 追踪方面面临挑战,特别是在部分遮阳条件下 (PSC).
- 现有的群集智能技术 (SIT) 可能会表现出缓慢的追踪时间和显著的启动振荡.
- 有效的GMPP跟踪对于最大限度地提高光伏系统的能源产量至关重要.
研究的目的:
- 提出一个改进的优化算法 (IGOA),以在PSC下在光伏系统中进行强大的GMPP跟踪.
- 在速度,准确性和稳定性方面,评估IGOA与已有的SIT相比的性能.
- 为了证明IGOA在减少跟踪时间和启动振荡方面的有效性.
主要方法:
- 实施一个改进的虫优化算法 (IGOA).
- 使用灰狼优化 (GWO),搜索算法 (CSA),Salp Swarm算法 (SSA) 和基于PSO (ISSAPSO) 的改进SSA进行比较分析.
- 在 MATLAB/SIMULINK 中模拟一个六个太阳能电池板的光伏系统,在均和部分阴影条件下,峰值各不相同.
- 对跟踪时间,结算时间,故障率和启动振荡的统计分析.
主要成果:
- IGOA在0.07秒内实现了GMPP跟踪,在0.12秒内实现了输出结算,比其他方法高出25%至96%.
- IGOA在不同数量的搜索代理 (4-6) 中显示了0%的跟踪失败率.
- 在拟议的IGOA中观察到微不足道的启动振荡.
结论:
- 改进的优化算法 (IGOA) 为在部分遮阳下在光伏系统中进行GMPP跟踪提供了优质的解决方案.
- IGOA显著减少了跟踪时间和启动振荡,提高了整体系统效率.
- 这项研究为光伏系统安装人员提供了宝贵的工具,以最大限度地提高能源发电量.
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