Transforming Electroluminescence Images to Current-Voltage (IV) Curves Using Deep Learning

作者：Brandon Byford, Jennifer L. Braid, Laura E. Boucheron, Bruce H. King · 年份：2024 · DOI：10.1109/pvsc57443.2024.10748790 · 被引用次数：2 · 研究领域：CCD and CMOS Imaging Sensors、Integrated Circuits and Semiconductor Failure Analysis

System health monitoring is an essential task in the operation and maintenance of any photovoltaic (PV) system. Typically, electroluminescence (EL), thermal imaging, and current-voltage (IV) curve analyses are used to analyze PV modules with each providing unique insights into system health. While it is relatively easy to acquire an EL or thermal image of a panel in-situ, acquisition of IV curves requires electrical disconnection of the panel from the array and either removal to a solar simulator or characterization and correction for the incident solar spectrum and intensity. In this work we show that, with the use of a transfer-learned Swin transformer model, we can predict accurate IV curves from EL images. Extracting single diode equation parameters from the predicted IV curves yielded an error less than 1% ± 1% for the maximum power point (MPP), short-circuit current$I_{\mathbf{sc}}$, open-circuit voltage$V_{\mathbf{oc}}$and photocurrent I. The series resistance$R_{\mathbf{s}}$and number of series cells$nN_{\mathbf{s}}V_{\mathbf{th}}$denoted as$N$were predicted with errors of ~5% ±7% and ~3% ±2%, respectively. Prediction of the shunt resistance$R_{\mathbf{sh}}$and dark current$\boldsymbol{I}_{\mathbf{o}}$yielded larger errors, likely due to sensitivity to small changes in the IV curve.