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Detecting heterogeneity in PV modules from massive real-world "step" I-V curves: A machine learning approach | |
Hu, Yang1; Schnabel, Erdmut2; Koehl, Michael2; French, Roger H.1; Peshek, Timothy J.1 | |
通讯作者 | Hu, Yang |
会议名称 | 43rd IEEE Photovoltaic Specialists Conference (PVSC) |
会议日期 | JUN 05-10, 2016 |
会议地点 | Portland, OR |
英文摘要 | We demonstrate that I-V curves with bypass diodes in forward bias can be useful in learning the heterogeneity in PV modules. In the laboratory-based experiments, we show that heterogeneity in a PV module can be detected from "step" IV curves that are collected under non-uniform irradiance. On the other hand, heterogeneous cell performance can lead to bypassing even under uniform irradiance. This hypothesis was tested using a fabricated 4-cell mini-module with cells that were engineered to have highly heterogeneous front contact resistivity and a SPICE-based circuit model. We find good agreement between the experimentally determined curve and simulations. We illustrate a technique for automatically classifying and analyzing massive real-world I-V curves and for gaining insights into the performance of PV modules. By classifying 3.7 million I-V curves, we demonstrate the occurrence of "step" I-V curves under two irradiance conditions: under non-uniform irradiance condition, mirror augmented PV module in Cleveland, Ohio; and under uniform irraidance condition at the Negev Desert, Israel, Gran Canaria, Spain and Mount Zugspitze, Germany. Under the uniform irradiance conditions, we found that the percentage of "step" I-V curves in all three I-V curve types gradually increase over time. This indicates the electrical characteristics within a PV module change from homogeneous to heterogeneous. Since the "step" I-V curves have a lower maximum power and a lower fill factor than normal I-V curves at the same irradiance condition, the heterogeneity in I-V module directly cause power degradation. |
来源出版物 | 2016 IEEE 43RD PHOTOVOLTAIC SPECIALISTS CONFERENCE (PVSC) |
ISSN | 0160-8371 |
出版年 | 2016 |
页码 | 279-284 |
EISBN | 978-1-5090-2724-8 |
出版者 | IEEE |
类型 | Proceedings Paper |
语种 | 英语 |
国家 | USA;Germany |
收录类别 | CPCI-S |
WOS记录号 | WOS:000399818700059 |
WOS类目 | Energy & Fuels ; Engineering, Electrical & Electronic ; Physics, Applied |
WOS研究方向 | Energy & Fuels ; Engineering ; Physics |
资源类型 | 会议论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/305240 |
作者单位 | 1.Case Western Reserve Univ, Dept Mat Sci & Engn, Cleveland, OH 44106 USA; 2.Fraunhofer Inst Solar Energy Syst ISE, Heidenhofstr 2, D-79110 Freiburg, Germany |
推荐引用方式 GB/T 7714 | Hu, Yang,Schnabel, Erdmut,Koehl, Michael,et al. Detecting heterogeneity in PV modules from massive real-world "step" I-V curves: A machine learning approach[C]:IEEE,2016:279-284. |
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