Single luminescence image plus lightweight AI maps PV module power loss at about 0.5% error
A Zhejiang University team led by Dongchen Lan published research in Matter & Light, selected as the journal's October cover: a lightweight AI model first judges from a single luminescence image whether a module's degradation is dominated by defect recombination or by series-resistance losses, and a device physics model then reconstructs local electrical behavior and the power-loss distribution. Validated on about 300 field-recovered crystalline-silicon modules from different production batches and operating environments, the reconstructed maximum power differed from laboratory standard current-voltage measurements by roughly 0.5% overall. The team also tested the method at a 186 kW plant with 342 modules, capturing luminescence images at night from a drone-mounted camera; in one representative area, 24 modules averaged 7.53% power loss, with some severely degraded cell regions near 25%. The team says next steps focus on using the method for routine plant operations and maintenance inspection, and the paper states the approach can extend to perovskite and tandem modules but requires redesign and revalidation.
The report notes that quantitative luminescence analysis previously required multiple images taken under different operating conditions, and that this measurement burden limited inspection efficiency and coverage at large plants; the study targets single-image quantitative analysis instead.
预测高达 300 块光伏组件的输出功率，总体误差仅有 0.5%。这个数字来自浙江大学兰东辰教授团队发表在 Matter & Light 上的研究。他们仅凭一张发光图像，就能对光伏组件内部功率损失做定量分析，并在实际光伏电站通过无人机采集图像做了验证，相关论文被选为 Matter & Light 期刊 10 月刊封面。
此前，使用发光成像做定量分析的时候，需要在不同工作条件之下拍摄多张图像。对于大型电站来讲，这种测量负担限制了检测效率以及覆盖面积。该团队告诉 DeepTech，他们的方法是先识别出组件的主导衰减机制，然后再用器件物理模型反推功率损失分布，这样一来一张图像就够了。
具体来说，他们先是让一个轻量级的 AI 模型看一张发光图像，借此判断这块组件衰减主要由哪类机制主导。光伏组件衰减依据其本质大致能被归为两类，第一类是电荷在器件内通过缺陷复合损失掉了，第二类是电流传输过程中因寄生电阻而损失。这两类损失机制在图像上呈现的空间图案存在区别，仅凭人工经验或轻量级 AI 可以识别出主导的是哪一类。
#Zhejiang University#Matter & Light#Dongchen Lan#Quantitative Energy (Nanjing)#University of New South Wales