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Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images

Journal Article · · IEEE Journal of Photovoltaics
 [1];  [1];  [2];  [2]
  1. New Mexico State Univ., Las Cruces, NM (United States)
  2. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
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Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current Isc, and open-circuit voltage Voc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance Rs demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance Rsh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
Grant/Contract Number:
NA0003525
OSTI ID:
2565765
Alternate ID(s):
OSTI ID: 2584136
Journal Information:
IEEE Journal of Photovoltaics, Journal Name: IEEE Journal of Photovoltaics Journal Issue: 4 Vol. 15; ISSN 2156-3381; ISSN 2156-3403
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

References (19)

Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements journal May 2021
Advanced analytics on IV curves and electroluminescence images of photovoltaic modules using machine learning algorithms journal September 2021
Onymous early‐life performance degradation analysis of recent photovoltaic module technologies journal August 2022
Photovoltaic modules fault detection, power output, and parameter estimation: A deep learning approach based on electroluminescence images journal November 2024
Automated defect identification in electroluminescence images of solar modules journal August 2022
Optimized estimator of the output power of PV cells using EL images and I–V curves journal November 2023
PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network journal January 2024
DPiT: Detecting Defects of Photovoltaic Solar Cells With Image Transformers journal January 2021
ImageNet: A large-scale hierarchical image database
  • Deng, Jia; Dong, Wei; Socher, Richard
  • 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2009 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2009.5206848
conference June 2009
Swin Transformer V2: Scaling Up Capacity and Resolution conference June 2022
Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification journal September 2019
Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images journal May 2020
Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images journal January 2022
Development of outdoor luminescence imaging for drone-based PV array inspection conference June 2017
PV Lifetime Project: Measuring PV Module PerformanceDegradation: 2018 Indoor Flash TestingResults
  • Stein, Joshua S.; Robinson, Charles; King, Bruce
  • 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) https://doi.org/10.1109/PVSC.2018.8547397
conference June 2018
Transforming Electroluminescence Images to Current-Voltage (IV) Curves Using Deep Learning conference June 2024
pvlib python: a python package for modeling solar energy systems journal September 2018
Automated Detection of Solar Cell Defects with Deep Learning conference September 2018
Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants journal January 2022

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