A deep learning approach to photovoltaic cell defect classification

Banda, Paul; Barnard, Lynette

doi:10.1145/3278681.3278707

Cited by 9 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes the solar cell produce less energy. [7][8][9] Hot spots Any flaw in solar cells, such as fractures, improperly soldered junctions, and abnormalities, leads to greater resistance and hot spots. Hot spots have severe consequences, such as burned scars that destroy solar cells and back sheets if they are not controlled, eventually leading to fire.…”

Section: Defectsmentioning

confidence: 99%

“…This assumption is valid whenever the panels are adequately separated, but it fails if the panels are frequently near and the connections become heated on a routine basis. This approach is tested in a real-time solar power plant with different kinds of sensors [6,7]. Temperature defects such as hot spots and other defects are identified by capturing the individual PV modules through infrared (IR) images and RGB images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Photovoltaic (PV) boards are a perfect way to create eco-friendly power from daylight. The defects in the PV panels are caused by various conditions; such defective PV panels need continuous monitoring. The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants. In general, conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation. The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process. To increase the accuracy and to reduce the processing time, a new Convolutional Neural Network (CNN) architecture is required. Hence, in the present work, a new Real-time Multi Variant Deep learning Model (RMVDM) architecture is proposed, and it extracts the image features and classifies the defects in PV panels quickly with high accuracy. The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images. The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person. The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset. The results show that 98% of the accuracy and recall values in the fault detection and classification process.

show abstract

Section: Defectsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This then enables the algorithms to automatically identify faults in new images, almost independent of cell sizes and module layouts (see figures 18(c) and (d)). The defects that are identified and classified by machine learning include micro-cracks, finger interruption, busbar corrosion, black core, cold soldering, shunts, and other defect types [33,37,96,97,[102][103][104][105][106]. Another machine learning-based approach is the use of principal component analysis to categorise modules by features and faults [107].…”

Section: Pv Module Conditionsmentioning

confidence: 99%

Outdoor luminescence imaging of field-deployed PV modules

Kunz¹,

Schlipf²,

Fladung³

et al. 2022

Prog. Energy

View full text Add to dashboard Cite

Solar photovoltaic installations have increased exponentially over the last decade and are now at a stage where they provide humanity with the greatest opportunity to mitigate accelerating climate change. For the continued growth and success of photovoltaic energy the reliable inspection of solar power plants is an important requirement. This ensures the installations are of high quality, safe to operate, and produce the maximum possible power for the longest possible plant life. Outdoor luminescence imaging of field-deployed PV modules provides module image data with unparalleled fidelity and is therefore the gold standard for assessing the quality, defect types, and degradation state of field-deployed PV modules. Several luminescence imaging methods have been developed and some of them are already routinely used to inspect solar power plants. The preferred luminescence inspection method to be used depends on the required image resolution, the defect types that need to be identified, cost, inspection throughput, technological readiness, and other factors. Due to the rich and detailed information provided by luminescence imaging measurements and modern image analysis methods, luminescence imaging is becoming an increasingly important tool for PV module quality assurance in PV power plants. Outdoor luminescence imaging can make valuable contributions to the commissioning, operation, and assessment of solar power plants prior to a change of ownership or after severe weather events. Another increasingly important use of these technologies is the cost-effective end-of-life assessment of solar modules to enable a sustainable circular economy.

show abstract

“…Under this background, two emerging renewable energy utilization technologies, photovoltaic (PV, referred to as photovoltaic) and solar thermal technology (concentrating solar power, referred to as CSP), are about to usher in rapid development. P. Banda studied the deep learning method of photovoltaic cell defect classification [1]. Antonio Greco researched a photovoltaic power plant panel inspection method based on deep learning [2].…”

Section: Research Background and Its Significancementioning

confidence: 99%

Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

Meng

Zhou

Li³

et al. 2021

MATEC Web Conf.

View full text Add to dashboard Cite

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

show abstract

A deep learning approach to photovoltaic cell defect classification

Cited by 9 publications

References 8 publications

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Outdoor luminescence imaging of field-deployed PV modules

Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

Contact Info

Product

Resources

About