Intelligent detection methods are used for PV module fault diagnosis to separate faults from operating data through intelligent classification algorithms, i.e., random forest, artificial neural network, decision tree, etc. (Zhicong et al., 2018, Li et al., 2021, Rabah and Samir, 2018). These methods have relatively low cost and good real-time
Section 3 provides the main fault detection and diagnosis strategies. Section 4 describes various PV FDD methods in the literature, including thermography as one of the
This paper investigates the dynamic parametric estimation of a solar cell system, by using balancing composite motions optimization (BCMO). Conventional fault detection methods in photovoltaic
Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detection December 2024 Data in Brief 57(111184):1-10
To tackle the issues of false positives and missed detections arising from inconsistent defect scales and complex, variable background textures in photovoltaic module
Aiming at improving the effectiveness and real-time performance of the fault detection of the photovoltaic (PV) array under different outdoor conditions, a novel method based on Grubbs criterion and local outlier factor (G-LOF) method is proposed in this
The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and diagnosis mechanisms. Traditional methods, relying on manual inspections and standard electrical
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.
In the configuration, Fig. 6, multiple PV cells form a string connected to a bypass diode. Notably, each string encompasses one-third of the total PV cells in the module, covering a corresponding one-third area of the PV module''s surface (S 1, S 2, S 3). In the event of shading occurring on a cell within a string, the bypass diode activates.
Hence, this paper is dedicated to reviewing recent advancements in monitoring, modeling, and fault detection methods for PV systems. It encompasses diverse PV system types, including grid-connected, stand-alone, and hybrid configurations, and delves into the latest data acquisition and monitoring techniques.
Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV
Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative
Different statistical outcomes have affirmed the significance of Photovoltaic (PV) systems and grid-connected PV plants worldwide. Surprisingly, the global cumulative installed capacity of solar PV systems has massively increased since 2000 to 1,177 GW by the end of 2022 .Moreover, installing PV plants has led to the exponential growth of solar cell deployment
Due to their high efficiency, photovoltaic (PV) cells can power the Internet of Things (IoT) devices, including sensors, actuators, and communication devices.
In their study, Tan et al., (2024) presented the RAFBSD detector, which incorporates sophisticated deep learning methods to precisely detect flaws in PV cells. These experiments demonstrate that the PVEL-AD dataset improves the mAP50 to 71.1 % of defect detection in PV cells. PV fault detection is an essential activity for guaranteeing the
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...
This fault classification method can be employed in real-time with minimal computation overhead when applied to a large PV system. In Schuss et al. (2016), In Xie et al. (2023) the issue of solar cell defect detection is discussed, which is challenging due to variations in production schemes and impurities on the surface of polycrystalline
Photovoltaic (PV) arrays have output characteristics such as randomness and intermittency, and faults can seriously affect the safe operation of the power system. In order to improve the comprehensive performance of the PV array fault diagnosis model, a new intelligent online fault monitoring method for PV arrays is proposed in this paper.
the solar cell that can reduce the efficiency of the cell. Scratches can occur during the manufacturing process or during installation and maintenance of the solar panel. 2.7 BLACK CELL Black cell fault in solar cells is a condition where a solar cell appears black and does not produce any electrical output. This can
Therefore, a suitable fault detection system should be enabled to minimize the damage caused by the faulty PV module and protect the PV system from various losses.
The method utilizes image processing techniques and principal component analysis for fault detection in PV tracking systems. Abubakar et al. also proposes a novel method of fault detection in PV arrays and inverter faults
Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is
To improve the accuracy of fault detection in photovoltaic To make the detection method of photovoltaic module defects be Zhang, M. & Yin, L. Solar cell surface defect detection based on
In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems. are analyzed. To extract major research
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
Solar energy generation Photovoltaic modules that work reliably for 20–30 years in environmental conditions can only be cost-effective. The temperature inside the PV cell is not uniform due to an increase in defects in the cells. Monitoring the heat of the PV panel is essential. Therefore, research on photovoltaic modules is necessary. Infrared thermal imaging (IRT) has
One way of examining surface defects on photovoltaic modules is the Electroluminescence (EL) imaging technique. The data set used in this work is an open data set for fault detection and classification of photovoltaic
Photovoltaic (PV) fault detection and classification are essential in maintaining the reliability of the PV system (PVS). Various faults may occur in either DC or AC side of the
The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses.
Despite their significant environmental benefits, solar photovoltaic (PV) systems are susceptible to malfunctions and performance degradation. This paper addresses detecting and diagnosing faults from a dataset representing a 250 kW PV power plant with three types of faults. A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships
In the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is significant. The purpose of this work is the study and implementation of such an algorithm, for the detection as many as faults arising on the DC side
Review recent advancements in monitoring, modeling, and fault detection for PV systems. Covers grid-connected, stand-alone, and hybrid PV systems, exploring data
Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address...
The method utilizes image processing techniques and principal component analysis for fault detection in PV tracking systems. 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 .
Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an
The photovoltaic technology industry is a key development field in response to global renewable energy demands. The efficiency of fault detection in solar cells, a core component, is vital. Traditional manual fault detection is inefficient and costly, and existing deep learning models lack accuracy and speed. To address these problems, this study proposes the ESD-YOLOv8
This paper proposes a novel PV defect detection method using attention mechanisms and transformers within the YOLOv8 object detection framework. systems through fault detection in solar cells
tecture in damage detection with object detection methods in PV and wind turbines, and an average sensitivity of 0.79 was obtained in the research performed by Y ahya et al. [ 19
fault detection and diagnosis strategies. Section 4 describes various PV FDD methods in the literature, including ther-mography as one of the most promising methods. Section 5 covers different artificial intelligence techniques that are used in fault detection of PV systems. Section 6 is the future work and conclusion of the paper which provide a
Maintenance and monitoring of solar photovoltaic (PV) systems are essential for enhancing reliability, extending lifespan, and maintaining efficiency. Some defects in PV cells cannot be detected through output
In addition, it can be found from the data in Table 2 that the CNN based solar cell fault warning could achieve diagnosis in 0.1 s at the fastest, and the longest diagnostic time was 7.9 s, bringing extremely high efficiency to solar cell fault warning. Compared to traditional methods, the fastest diagnostic time for statistical analysis was 9.
According to this type, fault detection and categorization techniques in photovoltaic systems can be classified into two classes: non-electrical class, includes visual and thermal methods (VTMs) or traditional electrical class , as shown in Fig. 4. PV FDD Categories and some examples
Results show that the method is able to detect faults in a PV array, and it was demonstrated experimentally for a SS-PVA. In a fault detection method based on WT and ANN is developed for an ungrounded PV system. The designed method is able to detect and localise GF and LL faults in a PVA.
The reliable performance and efficient fault diagnosis of photovoltaic (PV) systems are essential for optimizing energy generation, reducing downtime, and ensuring the longevity of PV installations.
Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly.
The VarifocalNet is an anchor-free detection method and has higher detection accuracy 5. To further improve both the detection accuracy and speed for detecting photovoltaic module defects, a detection method of photovoltaic module defects in EL images with faster detection speed and higher accuracy is proposed based on VarifocalNet.
One of the prominent methods employed for fault diagnosis in PV modules, strings, or arrays is the current and voltage (I–V) characteristics analysis (I-VCA) . This method is based on the comparison between the measured I–V characteristics, and the expected I–V curve derived from behavioral models of the PV module [127, 192].
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