Review of current state-of-the-art research on photovoltaic Soiling, anti-reflective coating, and solar roads deployment supported by a pilot experiment on a PV Road
The objective of this review paper is to provide an overview of the current state-of-the-art in
solar road deployment, including the availability of anti-reflection and anti-soiling coating …
solar road deployment, including the availability of anti-reflection and anti-soiling coating …
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 …
Investigating the impact of cracks on solar cells performance: Analysis based on nonuniform and uniform crack distributions
M Dhimish, V d'Alessandro… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The article investigates the detrimental effect of nonuniform and uniform crack distributions
over a solar cell in terms of open-circuit voltage (), short-circuit current density (), and output …
over a solar cell in terms of open-circuit voltage (), short-circuit current density (), and output …
[HTML][HTML] Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots
M Dhimish - Case Studies in Thermal Engineering, 2021 - Elsevier
Photovoltaic (PV) hot-spots is a reliability problem in PV modules, where a cell or group of
cells heats up significantly, dissipating rather than producing power, and resulting in a loss …
cells heats up significantly, dissipating rather than producing power, and resulting in a loss …
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 …
Automatic pixel-wise detection of evolving cracks on rock surface in video data
Accurately detecting the presence and evolving boundaries of cracks on rock surfaces is
critical for understanding the behavior of crack evolutions and facture mechanism of rock …
critical for understanding the behavior of crack evolutions and facture mechanism of rock …
[PDF][PDF] Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants.
W Tang, Q Yang, W Yan - CMES-Computer Modeling in …, 2022 - cdn.techscience.cn
Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has
become a standard test procedure during the process of production, installation, and …
become a standard test procedure during the process of production, installation, and …
Impact of solar cell cracks caused during potential-induced degradation (PID) tests
Potential-induced degradation (PID) of photovoltaic (PV) modules is one of the most severe
types of degradation in modern modules, where power losses depend on the strength of the …
types of degradation in modern modules, where power losses depend on the strength of the …
Recovery of photovoltaic potential-induced degradation utilizing automatic indirect voltage source
Potential-induced degradation (PID) of photovoltaic (PV) modules is one of the most severe
types of degradation in modern modules. PID can affect crystalline silicon PV modules, and …
types of degradation in modern modules. PID can affect crystalline silicon PV modules, and …
[HTML][HTML] High-Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model
L Aktouf, Y Shivanna, M Dhimish - Solar, 2024 - mdpi.com
This study presents an advanced defect detection approach for solar cells using the
YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell …
YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell …