一本关于在各种部分阴影条件下为太阳能光伏系统设计和实施混合MPPT控制器的小说
Chakarajamula Hussaian Basha1, Madhu Palati2, C Dhanamjayulu3
1NITTE Meenakshi Institute of Technology (Autonomous), Bengaluru, India.
Scientific reports
|January 18, 2024
概括
本研究探讨了光伏 (PV) 系统的混合优化控制器,以最大限度地提高部分阴影下的功率输出. 修改后的基于灰狼优化的ANFIS方法在追踪最大功率点 (MPP) 方面表现出卓越的性能.
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:
- 化石燃料的依赖导致环境污染和资源枯竭.
- 光伏 (PV) 系统提供了一个可持续的替代方案,但在最大限度地提取电力方面面临着挑战,特别是在部分遮阳条件下.
- 太阳能系统的非线性特征使得达到最大电压和最大输出功率的过程变得复杂.
研究的目的:
- 在光伏系统中研究和比较各种混合优化控制器用于最大功率点跟踪 (MPPT).
- 在不同的部分遮阳场景下评估不同MPPT方法的性能.
- 确定最有效的MPPT技术,以提高光伏系统的能源产量.
主要方法:
- 使用MATLAB/Simulink对MPPT进行各种混合优化控制器的模拟.
- 基于关键性能指标的MPPT方法的比较分析,例如振荡,功率提取,沉降时间,精度和复杂性.
- 基于灰狼优化修改的适应性神经模糊推理系统 (ANFIS) 混合MPPT方法的实施.
主要成果:
- 修改后的基于灰狼优化的ANFIS混合MPPT方法,与其他技术相比,表现出更高的性能.
- 评估的关键性能指标包括在最大功率点 (MPP) 上的振荡,输出功率提取效率和跟踪速度.
- 拟议的方法证明了更好的错误查找准确性和减少了在部分遮阳下跟踪MPP的复杂性.
结论:
- 修改后的基于灰狼优化 (Grey Wolf Optimization) 的ANFIS混合MPPT控制器对于在部分遮阳下最大限度地从光伏系统中提取电力非常有效.
- 这种先进的MPPT技术为太阳能发电的效率和可靠性提供了显著的改进.
- 该研究验证了混合优化算法的潜力,以应对光伏系统运行的挑战.
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