[HTML][HTML] Machine learning for advanced characterisation of silicon photovoltaics: A comprehensive review of techniques and applications
Accurate and efficient characterisation techniques are essential to ensure the optimal
performance and reliability of photovoltaic devices, especially given the large number of …
performance and reliability of photovoltaic devices, especially given the large number of …
Progress in module level quantitative electroluminescence imaging of crystalline silicon PV module: A review
Electroluminescence (EL) imaging is a prominent tool for obtaining qualitative and
quantitative information of defects and degradations in a crystalline silicon (c-Si) PV module …
quantitative information of defects and degradations in a crystalline silicon (c-Si) PV module …
Dual spin max pooling convolutional neural network for solar cell crack detection
This paper presents a solar cell crack detection system for use in photovoltaic (PV) assembly
units. The system utilizes four different Convolutional Neural Network (CNN) architectures …
units. The system utilizes four different Convolutional Neural Network (CNN) architectures …
Rapid testing on the effect of cracks on solar cells output power performance and thermal operation
This work investigates the impact of cracks and fractural defects in solar cells and their
cause for output power losses and the development of hotspots. First, an …
cause for output power losses and the development of hotspots. First, an …
Outdoor luminescence imaging of field-deployed PV modules
O Kunz, J Schlipf, A Fladung, YS Khoo… - Progress in …, 2022 - iopscience.iop.org
Solar photovoltaic (PV) installations have increased exponentially over the last decade and
are now at a stage where they provide humanity with the greatest opportunity to mitigate …
are now at a stage where they provide humanity with the greatest opportunity to mitigate …
Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection
Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their
performance and reliability. The development of convolutional neural networks (CNNs) has …
performance and reliability. The development of convolutional neural networks (CNNs) has …
An empirical investigation on the correlation between solar cell cracks and hotspots
In recent years, solar cell cracks have been a topic of interest to industry because of their
impact on performance deterioration. Therefore, in this work, we investigate the correlation …
impact on performance deterioration. Therefore, in this work, we investigate the correlation …
Efficient cell segmentation from electroluminescent images of single-crystalline silicon photovoltaic modules and cell-based defect identification using deep learning …
HH Lin, HK Dandage, KM Lin, YT Lin, YJ Chen - Sensors, 2021 - mdpi.com
Solar cells may possess defects during the manufacturing process in photovoltaic (PV)
industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing …
industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing …
High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network
Harvesting solar energy through photovoltaic (PV) power systems plays an important role in
achieving the goal of carbon neutrality. However, the early microdefects in PV cells …
achieving the goal of carbon neutrality. However, the early microdefects in PV cells …
Machine learning based identification and classification of field-operation caused solar panel failures observed in electroluminescence images
S Bordihn, A Fladung, J Schlipf… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Failure or degradation effects lead to power losses in solar panels during their field
operation and are identified commonly by electroluminescence (EL) imaging. Some failures …
operation and are identified commonly by electroluminescence (EL) imaging. Some failures …