Photovoltaic cell automatic detection system

A PV cell defect detector combined with transformer and attention

Automated defect detection in electroluminescence (EL) images of

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A photovoltaic cell defect detection model capable of

Deep learning methods of PV defect detection. Convolutional neural networks (CNNs) have become a prominent tool in the automatic detection of surface defects in photovoltaic (PV) cells.

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A review of automated solar photovoltaic defect detection systems

Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems

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An efficient CNN-based detector for photovoltaic module cells

To address this issue, we propose a novel method for efficient PV cell defect

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Deep learning based automatic defect identification of photovoltaic

CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy, 189 (2019), Article 116319. View PDF View article View in Scopus Google Scholar. Alec et al., 2015. R. Alec, M. Luke, C. Soumith. Unsupervised representation learning with deep convolutional generative adversarial networks. Comput. Sci.

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An efficient and portable solar cell defect detection system

In this study, a novel system for discovering solar cell defects is proposed, which is compatible with portable and low computational power devices. It is based on K -means, MobileNetV2 and linear discriminant algorithms to cluster solar cell images and develop a detection model for each constructed cluster.

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Deep-Learning-Based Automatic Detection of Photovoltaic Cell

Deitsch et al. proposed two deep-learning-based methods for the automatic detection of PV cell defects with convolutional neural networks (CNNs) and SVMs; the results showed that CNN classifier detection has higher accuracy.

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A PV cell defect detector combined with transformer and

Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and...

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BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell

Abstract: The multiscale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as network deepens. To address this problem, an attention-based top-down and bottom-up architecture is developed to accomplish multiscale feature fusion. This architecture, called bidirectional

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Deep Learning-Based Defect Detection for Photovoltaic Cells

M. Y. Demirci, N. Beşli, A. (2019) Gümüşçü, Defective PV cell detection using deep transfer learning and EL imaging, Int Conf Data Sci, Mach Learn and Stat 2019 (DMS-2019) 2019. Google Scholar M. W. Akram et al (2019) CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 189.

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Real Time Fault Detection in Photovoltaic Cells by Cameras

The method is based on the following three steps, whose output is shown in Fig. 1: (i) during the Preprocessing step, the lines in the images (white lines in Fig. 1b) are extracted and used to align the image and to (ii) find out the panels in the modules (identified by the white rectangles in Fig. 1c). Finally, for each detected panel, the (iii) detection of the hot spots is

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Defect Detection in Photovoltaic Module Cell Using CNN Model

IoT-Enabled Energy Efficiency Assessment of Renewable Energy Systems and Micro-grids in Smart Cities . Conference paper. Defect Detection in Photovoltaic Module Cell Using CNN Model. Conference paper; First Online: 28 May 2024; pp 403–411; Cite this conference paper; Download book PDF. Download book EPUB. IoT-Enabled Energy

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Fault detection and diagnosis methods for photovoltaic systems

Monitoring systems (MS) are crucial for controlling, supervising and performing fault detection of photovoltaic plants, so many systems have been recently proposed aiming to perform a real-time monitoring of PV plants (PVP); in this context the common reference documents are the standard IEC 61724 [47], titled: Photovoltaic system performance

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Fault diagnosis of photovoltaic systems using artificial

Solar cells have the option to be linked either in a series or in parallel with basic electrical protections such as bypass diodes to form a complete photovoltaic module. To ensure long-term functionality, solar cells are encapsulated in a secure environment with additional protection, known as a photovoltaic module [8].

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A review of automated solar photovoltaic defect detection systems

Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique.

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An automatic detection model for cracks in photovoltaic cells

Early detection of faults in PV modules is essential for the effective operation of the PV systems and for reducing the cost of their operation. In this study, an improved version of You Only Look Once version 7 (YOLOv7) model is developed for the detection of cell cracks in PV modules. Detecting small cracks in PV modules is a challenging task.

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Deep-Learning-Based Automatic Detection of Photovoltaic Cell

Deitsch et al. proposed two deep-learning-based methods for the automatic

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An efficient CNN-based detector for photovoltaic module cells

To address this issue, we propose a novel method for efficient PV cell defect detection. Firstly, we utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to improve EL image contrast, making defect features become more distinguishable. Secondly, we propose a lightweight defect detector using EfficientNet-B0 as its backbone.

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Fault diagnosis of photovoltaic systems using artificial

Taking into account the numerous factors that inﬂuence the fault detection processes in photovoltaic (PV) systems, several authors have proposed conventional reviews as a means to understand current fault detection research in photovoltaic sys-tems[1,37,39,45,66,69,82–93]. These reviews highlight the rapid replacement of conventional

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An automatic detection model for cracks in

Photovoltaic (PV) systems have a number of advantages over traditional energy sources, such as the reduction of dependence on fossil fuels and the increased efficiency of energy production. The use of PV systems

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Automated detection and tracking of photovoltaic modules

Real-time detection of PV modules in large-scale plants under varying lighting conditions. Automatic monitoring and evaluation of individual PV module performance. Development of monitoring and simulation methods using 3D remote sensing data.

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Automatic Faults Detection of Photovoltaic Farms: solAIr, a

energies Article Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images Roberto Pierdicca 1,*, Marina Paolanti 2, Andrea Felicetti 2, Fabio Piccinini 1 and Primo Zingaretti 2 1 Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, 60131 Ancona, Italy; [email protected]

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AI-assisted Cell-Level Fault Detection and Localization in Solar PV

The objective of this work is to build an End-to-End Fault Detection system to detect and localize faults in solar panels based on their Electroluminescence (EL) Imaging. Today, the majority of fault detection happens through manual inspection of EL images. To this end, we propose the design and implementation of an end-to-end system that

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Online automatic anomaly detection for photovoltaic systems

Wang et al. (2021) suggested automatic anomaly detection for PV systems utilizing thermography imaging together with low-rank matrix decomposition. The algorithm utilized in this technique was the

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