一个基于传感器的低效和强大的MPPT非线性控制器,用于在快速变化的气候条件下运行的电网集成光伏能源系统
Abdulaziz Almalaq1, Andres Annuk2, Tao Jin3
1Department of Electrical Engineering, University of Hail, 55211, Hail, Saudi Arabia.
Scientific reports
|November 25, 2025
概括
本研究介绍了一种基于传感器的非线性最大功率点跟踪 (MPPT) 控制器,用于光伏 (PV) 系统,在没有昂贵的辐射传感器的情况下提高效率和稳定性. 新的控制器确保了光伏发电的更快的融合和更好的电网集成.
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
- 控制理论 控制理论
背景情况:
- 集成电网的光伏 (PV) 系统需要高效的最大功率点跟踪 (MPPT) 才能获得最佳的能源收获.
- 快速变化的气候条件给传统的MPPT控制器带来了挑战,通常需要昂贵的辐射传感器.
- 保持直流连接稳定性和电网电力质量对于可靠的光伏系统运行至关重要.
研究的目的:
- 为集网光伏系统提供基于传感器的简化非线性MPPT控制器.
- 在动态环境条件下实现快速和强大的MPPT性能.
- 为了提高直流连接的稳定性和电网电源质量,而不需要依赖辐射传感器.
主要方法:
- 开发了一种两级控制器,将数学辐射估计与辐射基函数神经网络相结合.
- 使用后退非线性控制器来强制执行最佳的参考电压.
- 在基于MATLAB/Simulink的100千瓦电力网联光伏系统上验证了控制器.
主要成果:
- 在阶段辐射变化下,控制器在7ms内实现了快速的最大功率点 (MPP) 追踪.
- 在42毫秒内恢复了直流链的稳定性,总波扭曲 (THD) 仍然低于0.1%.
- 在功率输出,主动功率传输和直流链接电压调节方面,性能优于扰乱和观察 (P&O),改进差分演变 (IDE) 和粒子群集优化 (PSO).
结论:
- 拟议的基于传感器的简化非线性MPPT控制器与现有方法相比,提供了更高的性能.
- 它为大型光伏系统提供了更快的融合,改进了电压调节,并提高了电网稳定性.
- 控制器是一个有希望的解决方案,用于在可变条件下运行的光伏系统的现实世界部署.
关键词:
退步算法的算法 退步算法整合到电网中的可再生能源系统.在MPPTT中,MPPT是MPPT,MPPT是MPPT.神经网络的神经网络光伏能源转换系统的光伏系统.太阳辐射强度传感器 太阳辐射强度传感器更多相关视频
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