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光伏发电技术 ★ 5.0

基于自供电配置的日间模式光伏组件与组串电致发光检测

Electroluminescence inspections of PV modules and strings by a self-powering configuration in daylight mode

作者 L.A.Carpintero · C.Terrados · D.González-Francés · K.P.Sulc · V.Alonso · M.A.González · O.Martínez
期刊 Solar Energy
出版日期 2025年1月
卷/期 第 301 卷
技术分类 光伏发电技术
相关度评分 ★★★★★ 5.0 / 5.0
关键词 Daylight EL enables inspections without dismounting panels reducing costs.
语言:

中文摘要

摘要 电致发光(EL)成像是一种广泛用于识别光伏(PV)组件中太阳电池缺陷的工具。传统的EL检测需要在黑暗环境中进行,并且要求拆卸组件,导致成本高昂且在物流上具有挑战性。日间电致发光(dEL)作为一种经济高效的替代方案应运而生,可在任意辐照条件下实现现场检测,无需拆卸组件,从而降低成本。然而,EL检测需要注入电流,因此必须依赖外部电源。已有研究提出使用双向逆变器等方案来应对这一挑战。本研究提出一种新颖的自供电dEL方法,利用电站中的其他光伏组串提供所需电流。该方法采用切换程序以滤除环境光干扰,能够在不拆卸组件、不使用外部电源的情况下完成整串组件的检测。在不同辐照条件下的实地测试结果表明,所获得的图像质量可与在受控暗室环境中获取的图像相媲美,验证了该方法的有效性及其在运行操作方面的优势。

English Abstract

Abstract Electroluminescence (EL) imaging is a widely used tool for identifying defects in the solar cells of photovoltaic (PV) modules. Traditional EL inspections require dark conditions and module disassembly, making them costly and logistically challenging. Daylight Electroluminescence (dEL) has emerged as a cost-effective alternative, enabling on-site inspections under any irradiance conditions without module dismounting and thereby reducing costs. However, EL inspections require current injection, necessitating an external power source. Solutions like bidirectional inverters have been proposed to address this challenge. This study proposes a novel self-powered dEL methodology that uses other PV strings in the plant to supply the necessary current. The method employs a switching procedure to filter ambient light and allows entire string inspection without dismounting modules or using external power. Field tests across various irradiance conditions show that the resulting images are comparable to those obtained in controlled darkroom environments, validating the method’s effectiveness and operational advantages.
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SunView 深度解读

该自供电日光电致发光检测技术对阳光电源SG系列逆变器及iSolarCloud智能运维平台具有重要应用价值。通过利用组串互供电流实现现场缺陷检测,无需外部电源和拆卸组件,可与阳光电源逆变器的MPPT优化技术协同,实现在线故障诊断。该方法可集成至iSolarCloud平台的预测性维护功能,结合IV曲线诊断和红外热成像,构建更完善的光伏电站智能运维体系,显著降低运维成本,提升发电效率和资产管理水平。