← 返回
利用冲击射流和多孔介质增强PVT/空气系统性能:一种结合机器学习预测的计算方法
Enhancing PVT/air system performance with impinging jet and porous media: A computational approach with machine learning predictions
| 作者 | Somayeh Davoodabadi Farahani · Mehdi Khademi Zar · As'ad Alizadeh |
| 期刊 | Applied Energy |
| 出版日期 | 2025年1月 |
| 卷/期 | 第 377 卷 |
| 技术分类 | 光伏发电技术 |
| 技术标签 | 机器学习 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | The combination of jets and porous foam gradient resulted in an improvement of electrical efficiency and thermal efficiency. |
语言:
中文摘要
热光伏系统(PVT)吸收太阳能并将其转化为电能。太阳能电池温度的升高会降低其发电效率。本研究采用多孔介质与冲击气流射流对光伏组件(PV)进行冷却,以降低太阳能电池的工作温度。考虑了不同布置形式的冲击射流系统(单点与多点),用于评估光伏组件的发电效率。研究考察了雷诺数(Re)、太阳辐射强度、孔隙率系数、达西数(Darcy number)、无量纲多孔层厚度、射流及注入位置、射流速度与角度等因素对PVT系统效率的影响。结果表明,多孔介质通过提高有效导热系数并降低对流热阻,对降低光伏组件温度和提升其发电效率具有积极作用。多孔介质特性对光伏发电效率的影响受多孔层厚度的调控。与均匀孔隙率相比,采用变孔隙率的多孔介质可使光伏发电效率最高提升6%。单点与多点布置的冲击射流可使光伏发电效率提升3%至26%。冲击射流在从光伏组件表面移除热量方面表现出显著潜力。将光伏组件与多射流系统集成时,其效率优于单射流模式。此外,基于本研究的数据,采用自适应神经模糊推理系统(ANFIS)模型以及高斯过程回归(GPR)等机器学习算法对发电效率进行了预测,结果表明GPR方法能够较好地逼近光伏系统的发电效率。
English Abstract
Abstract Thermal photovoltaic systems (PVT) absorb the sun's energy and convert it into electricity. Increasing the solar cell temperature reduces its efficiency. In the present research, the porous medium and impinging air jet is used for PV cooling to lessen the solar cell temperature. Different arrangements of impinging jet systems (single and multiple) have been considered to evaluate the electrical efficiency of PV. The effects of Re , solar radiation intensity , porosity coefficient, Darcy number , dimensionless porous layer thickness, jet and injection location, jet velocity and angle on PVT efficiency have been inspected. The results show that the porous medium has a positive effect on reducing the PV temperature and increasing the electrical efficiency of PV by increasing the effective thermal conductivity coefficient and reducing the convection resistance. The effect of the characteristics of the porous medium on the electrical efficiency of PV is influenced by porous layer thickness. Porous medium with variable porosity can improve the electrical efficiency of PV up to 6 % compared to uniform porosity. Impingement jet in single and multiple arrangement between 3 and 26 % to improve the electrical efficiency of PV. The impinging jet has a high potential in removing heat from PV. In the integration of PV with multiple jets, more efficiency can be obtained than the single jet mode. Also, by using the data of this survey and the adaptive Neuro-fuzzy inference system (ANFIS) model and Gaussian process regression (GPR) from machine learning algorithms , the electrical efficiency has been estimated and the GPR method has been able to approximate the electrical efficiency well.
S
SunView 深度解读
该PVT主动冷却技术对阳光电源SG系列光伏逆变器系统具有重要参考价值。研究表明冲击射流与多孔介质结合可提升电池效率达26%,这为我司1500V高功率系统的热管理优化提供新思路。其机器学习预测模型(GPR/ANFIS)可集成至iSolarCloud平台,实现光伏组件温度的智能预测与主动冷却控制,结合MPPT优化算法动态调整工作点,提升发电效率。该技术尤其适用于高辐照地区的大型地面电站及工商业屋顶项目,可降低温度损失,延长组件寿命,增强系统经济性。