The rapid development of the photovoltaic industry in recent years has made the efficient and accurate completion of photovoltaic operation and maintenance a major focus in recent studies. The key to photovoltaic operation and maintenance is the accurate multifault identification of photovoltaic panel images collected using drones. In this paper, PV-YOLO is proposed to …
The rapid development of the photovoltaic industry in recent years has made the efficient and accurate completion of photovoltaic operation and maintenance a major focus in recent studies. The key to photovoltaic operation and maintenance is the accurate multifault identification of photovoltaic panel images collected using drones. In this paper, PV-YOLO is proposed to …
Aiming at the problems of chaotic distribution of defect targets on photovoltaic panels, large scale span and blurred features, this paper improves the network structure based on the YOLOv5 model, which can better cope with the defect detection under various conditions. This paper mainly optimizes the following three aspects.
Most faults can be detected by the infrared temperature measurement method, but the infrared camera characteristics constrain it. This paper proposed a novel framework, consisting of image acquirement, image segmentation, fault orientation and defect warning, to remedy the limitations for PV module defects.
Accurate identification of solar photovoltaic (PV) rooftop installations is crucial for renewable energy planning and resource assessment. This paper presents a novel approach to automatically detect and delineate solar PV rooftops using high-resolution satellite imagery and the advanced Mask R-CNN (Region-based Convolutional Neural Network) architecture. The proposed …
Solar photovoltaic panel defect detection is an important part of solar... | Find, read and cite all the research you need on ResearchGate . Conference Paper. Improved Solar Photovoltaic Panel ...
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and costly ...
IoT (Internet of Things) are evolving technologies that have been studied for …
The proliferation of solar photovoltaic (PV) systems necessitates efficient strategies for inspecting and classifying anomalies in endoflife modules, which contain heavy metals posing environ- mental risks. In this paper, we propose a comprehensive approach integrating infrared (IR) imaging and deep learning techniques, including ResN et and custom CNN s. Our …
This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN) and the VGG16 architecture. The model effectively identifies physical and electrical changes, such as dust and bird droppings, and is implemented using the PyQt5 Python tool to create a ...
For the defect detection of solar panels, the main traditional methods are divided into artificial physical method and machine vision method. Byung-Kwan Kang et al. [6] used a suitable temperature control procedure to adjust the relationship between the measured voltage and current, and estimated the photovoltaic array using Kalman filter algorithm with a …
As a result of sustained investment and continual innovation in technology, project financing, and execution, over 100 MW of new photovoltaic (PV) installation is being added to global installed capacity every day since 2013 [6], which resulted in the present global installed capacity of approximately 655 GW (refer Fig. 1) [7].The earth receives close to 885 …
Aiming at the multi-defect-recognition challenge in PV-panel image analysis, this study innovatively proposes a new algorithm for the defect detection of PV panels incorporating YOLOv7-GX technology.
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.
Defect detection for photovoltaic (PV) modules is crucial in their production process, but the dataset quality and complex defects limit the accuracy and speed of the detection. In this paper, a solar PV module defect detection method was investigated using electroluminescence (EL) images. To reduce useless information in the EL images, a PV ...
Solar photovoltaic (PV) energy has gained significant attention and has undergone rapid global development in the past decade. The deployment of PV technology has expanded quickly, including both ...
Electroluminescence technology is a useful technique in detecting solar panels'' faults and determining their life span using artificial intelligence tools such as neural networks and others. In ...
Automated defect detection in electroluminescence (EL) images of …
Solar photovoltaic panel defect detection is an important part of solar photovoltaic panel quality inspection. Aiming at the problems of chaotic distribution of defect targets on photovoltaic panels, large scale span and blurred features, this paper improves the network structure based on the YOLOv5 model, which can better cope with the defect ...
IoT (Internet of Things) are evolving technologies that have been studied for enhanced fault detection and predictive analysis in the maintenance and environmental monitoring of solar power plants. This research work suggests a method based on MLTs (machine learning techniques) to analyze power data and predict faults for the maintenance of ...
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and...
Nearly all types of solar photovoltaic cells and technologies have developed dramatically, especially in the past 5 years. Here, we critically compare the different types of photovoltaic ...
This study aims to develop methods for detecting faults in photovoltaic panels using infrared solar module images. To achieve this goal, the "Efficientb0" model, a pre-trained deep learning network, has been preferred. The use of a pre-trained model has facilitated faster and more effective learning of the data. Another significant aspect ...
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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