In this paper, an algorithm based on thermal image processing, is proposed to extract the features of the PV cells in operation. These extracted features are then compared with the features of the...
Our objective is to identify unusual operating conditions in a photovoltaic string using only the voltage and current generated at its terminals.
Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an
SVMs are widely utilized for defect detection in solar PV modules, particularly for identifying cracks, hotspots, micro-cracks, and other internal failures. SVM models have been effectively applied to both thermal images and I-V curve data, achieving high accuracy rates—often above 97%—in various studies. For example, a study leveraging SVM
In this paper, an algorithm based on thermal image processing, is proposed to extract the features of the PV cells in operation. These extracted features are then compared
This paper proposes an equivalent circuit approach to detect abnormal behavior in PV panels. Any class of defects, affect one or more elements of AC equivalent circuit of a PV panel. Therefore, variations of those elements change frequency domain behavior of PV panels and can be detected visually.
IoT (Internet of Things) are evolving technologies that have been studied for enhanced fault detection and predictive analysis in the maintenance and environmental
While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely impact energy production, necessitating the rapid and effective detection of such faults.
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.
The Lock-in thermography-based method of fault rectification and detection has proved to be extremely efficient in locating the position of hotspots or regions where the heat is concentrated in the various components that are present in the PV module and also helps to detect the loss of power occurring in the cells present in the panel. The
While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely
This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN)
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
This paper proposes an equivalent circuit approach to detect abnormal behavior in PV panels. Any class of defects, affect one or more elements of AC equivalent circuit of a PV panel.
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
The Lock-in thermography-based method of fault rectification and detection has proved to be extremely efficient in locating the position of hotspots or regions where the heat is concentrated in the various components that are present in the PV module and also helps to
Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper
SVMs are widely utilized for defect detection in solar PV modules, particularly for identifying cracks, hotspots, micro-cracks, and other internal failures. SVM models have been
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.
The results obtained indicate that the proposed method has significant potential for detecting faults in photovoltaic panels. Training the model from scratch has allowed for better processing of infrared images and more precise detection of faults in the panels.
The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation.
This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems.
The faults in the PV panel, PV string and MPPT controller can be effectively identified using this method. The detection of fault is done by comparing the ideal and measured parameters. Any difference in measured and ideal values indicate the presence of a fault.
Abubakar et al. also proposes a novel method of fault detection in PV arrays and inverter faults by utilizing an Elman neural network (ENN), boosted tree algorithms (BTA), and statistical learning techniques . In the study performed by Kellil et al. , a fault detection system for classifying faults in PV modules is proposed.
In this study, the use of an artificial intelligence model is proposed to detect faults in photovoltaic panels. The study was conducted on a dataset consisting of images obtained from infrared solar modules, and the proposed model relies on deep learning techniques, with the Efficientb0 model as its primary foundation.
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