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在部分阴影条件下,在光伏能源系统中估计太阳辐射和定位最佳功率区域

Ambe Harrison¹, Njimboh Henry Alombah², Jean de Dieu Nguimfack Ndongmo³

¹Department of Electrical and Electronics Engineering, College of Technology (COT), University of Buea, P.O.Box Buea 63, Cameroon.

|July 31, 2023

概括

这项研究简化了光伏 (PV) 系统在部分阴影条件下 (PSC) 的最大功率点跟踪 (MPPT). 一种新的方法将多个最大功率转换为单个最佳功率区域,提高MPPT的效率.

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
光伏技术光伏技术

背景情况:

光伏 (PV) 系统需要高效的最大功率点跟踪 (MPPT) 来实现最佳的能源采集.
部分遮阳条件 (PSC) 在光伏曲线上引入多个最大功率点 (MPP),挑战传统的MPPT算法,如 Perturb 和 Observe 和增量导电.
现有的先进的MPPT算法通常涉及复杂性和性能之间的权衡.

研究的目的:

为在部分遮阳条件下 (PSC) 运行的光伏系统制定简化的MPPT方法.
将PSC下的复杂多重MPP环境转化为单一的MPP问题,使得使用更简单的MPPT算法成为可能.
在没有直接太阳辐射测量的情况下,引入一种新的,快速的方法来确定PSC下的全球MPP.

主要方法:

提出了一种新的方法来将PSC下的光伏系统中复杂的多重MPP问题减少到一个单一的MPP目标.
该方法涉及将PSC下的功率-电压 (P-V) 曲线转换为单个MPP的同等曲线.
使用基于智能神经网络的预测器,加上太阳辐射估计器,仅依赖光伏阵列电流和电压数据.

主要成果:

对735个部分遮阳模式的研究表明,有可能将光伏曲线的有效面积减少到8.2620%.
这种减少创造了一个包含单个MPP的最佳功率区域,适合简单的传统MPPT算法.
拟议的预测器可以准确地确定最佳功率区域,而无需直接,昂贵的太阳辐射量测量.

结论:

开发的方法有效地简化了PSC下的光伏系统的MPPT,通过将多个MPP整合到一个单一的最佳功率区域.
这种简化提高了传统MPPT算法的跟踪性能,在具有挑战性的部分遮阳场景中.
基于神经网络的预测器为MPP跟踪提供了成本效益和可靠的解决方案,独立于直接太阳辐射测量.

关键词:

辐射估计器的辐射估计器在MPPTT中,MPPT是MPPT,MPPT是MPPT.最佳的功率区域是最佳的功率区域.这就是PV的PV.部分遮阳条件部分遮阳条件

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