Search Results (74)

The growing deployment of photovoltaic (PV) systems worldwide has amplified the need for efficient, non-invasive diagnostic techniques to monitor their performance and ensure long-term reliability. Infrared (IR) thermography has emerged as a powerful tool for detecting thermal anomalies such as hotspots, cell mismatches, shading effects, and degradation in PV modules under real operating conditions. This review presents a comprehensive overview of recent advancements in thermographic analysis applied to PV diagnostics. It discusses the principles of thermal imaging, imaging protocols, and data interpretation techniques, alongside common thermal defects encountered in field and laboratory settings. Furthermore, the integration of irradiance mapping, drone-assisted surveys, and AI-based image analysis is examined for enhancing detection accuracy and scalability. The review also highlights standardization challenges, environmental influences, and emerging trends in automation and predictive maintenance. By consolidating current research, this study underscores the critical role of thermography in optimizing PV performance, reducing maintenance costs, and supporting the transition to smarter, more resilient solar energy infrastructures. Full article

Given the rising global demand for environmentally sustainable energy sources, solar photovoltaic (PV) power generation has emerged as a pivotal component of the energy transition. In PV systems, power conversion efficiency is degraded and operational lifespan reduced due to the presence of defective modules. Consequently, achieving accurate and efficient defect detection during PV module manufacturing is critical to ensuring product quality and reliability. To address this challenge, we propose MambaVSS-YOLOv11n, an electroluminescence (EL) image-based multi-defect detection method for PV modules. Our study utilizes a dataset containing six types of defects—Broken Gate, Cold Solder Joint, Black Spot, Scratch, Microcrack, and Suction Mark—to construct 692 labeled EL images of defective PV modules. The model integrates the Vision State Space (VSS) module from Mamba and optimizes the C3k2 Bottleneck structure to enhance fine-grained feature extraction, while employing Space-to-Depth Convolutional (SPD-Conv) Layer for downsampling to improve computational efficiency. Additionally, to address YOLOv11n’s limited generalization capability for small objects and complex backgrounds, we adopt the Inner Mask Distance Penalized Intersection over the Union (Inner-MDPIoU) loss function, which enhances detection accuracy and mitigates the impact of low-quality samples. Experimental results demonstrate that compared to YOLOv11n, MambaVSS-YOLOv11n reduces the number of parameters by 18.1%, while improving mAP@0.5 to 0.869 and mAP@0.5:0.95 to 0.637. This achieves model lightweighting while enhancing detection performance. These findings indicate that the model is well-suited for real-time defect detection in PV module production lines, providing PV manufacturers with a lightweight yet accurate and reliable solution for PV module defect inspection. Full article

Machine vision based on artificial intelligence technology is being actively utilized to reduce defect rates in the photovoltaic module production process. This study aims to propose a machine vision approach using EfficientNet-B0 for defective photovoltaic module detection. In particular, the proposed approach is applied to the electroluminescence (EL) operation, which identifies microcracks in PV modules by using polarization current. The proposed approach extracts low-level structures and local brightness variations, such as busbars, fingers, and cell boundaries, from a single convolutional block. Furthermore, the mobile inverted bottleneck convolution (MBConv) block progressively transforms defect patterns—such as microcracks and dark spots—that appear at various shooting angles into high-level feature representations. The converted image is then processed using global average pooling (GAP), Dropout, and a final fully connected layer (Dense) to calculate the probability of a defective module. A sigmoid activation function is then used to determine whether a PV module is defective. Experiments show that the proposed Efficient-B0-based methodology can stably achieve defect detection accuracy comparable to AlexNet and GoogLeNet, despite its relatively small number of parameters and fast processing speed. Therefore, this study will contribute to increasing the efficiency of EL operation in industrial fields and improving the productivity of PV modules. Full article

Accurate current–voltage (IV) characterization is essential for assessing photovoltaic (PV) module performance, yet conventional IV tracing requires physical contact and controlled conditions, limiting large-scale deployment. Electroluminescence (EL) imaging, while highly effective for detecting localized defects, remains largely qualitative and indirect in estimating actual PV module power loss. This study introduces a deep learning framework that directly predicts complete IV curves from EL images, transforming EL inspection into a quantitative, non-contact diagnostic tool. In this work, we propose a convolutional neural network (CNN) that learns the nonlinear mapping between paired EL images captured at 20% and 80% of the short-circuit current and the corresponding IV response. A total of 438 PV modules were used for model development, with performance evaluated on unseen data. The trained CNN reconstructs IV curves with high fidelity, achieving a validation accuracy of approximately 95% and low parameter deviations (<2% for key metrics such as maximum power point and fill factor). The model maintains consistent accuracy even when a single EL image is provided, supporting flexible field operation. Inference is rapid, requiring less than 0.5 s per PV module inspection, enabling real-time analysis. Full article

Photovoltaic systems (PV) are increasingly recognized as fundamental to the worldwide adoption of renewable energy technologies. Nonetheless, the efficiency and longevity of solar panels can be compromised by various anomalies, ranging from physical defects to environmental impacts. Early and accurate detection of these anomalies is crucial for maintaining optimal performance and preventing significant energy losses. This study presents SolarAttnNet, a novel convolutional neural network (CNN) architecture with integrated channel and spatial attention mechanisms for solar panel anomaly detection. The proposed model addresses the critical need for automated detection systems, which are crucial for maintaining energy production efficiency and optimizing maintenance. This approach leverages attention mechanisms that emphasize the most relevant features within thermal and visual imagery, improving detection accuracy across multiple anomaly types. SolarAttnNet is evaluated on three distinct solar panel datasets, demonstrating its effectiveness through comprehensive ablation studies that isolate the contribution of each architectural component. Experimental results show that SolarAttnNet achieves superior performance compared to state-of-the-art methods, with accuracy improvements of 3.9% on the PV Systems-AD dataset (94.2% vs. 90.3%), 3.6% on the InfraredSolarModules dataset (92.1% vs. 88.5%), and 3.5% on the RoboflowAnomalies dataset (89.7% vs. 86.2%) compared to baseline ResNet-50. For challenging subtle anomalies like cell cracks and PID, the proposed model demonstrates even more significant improvements with F1-score gains of 4.8% and 5.4%, respectively. Ablation studies reveal that the channel attention mechanism contributes a 2.6% accuracy improvement while spatial attention adds 2.3% across datasets. This work contributes to advancing automated inspection technologies for renewable energy infrastructure, supporting more efficient maintenance protocols and ultimately enhancing solar energy production. Full article

Photovoltaic (PV) systems represent one of the pillars of the global energy transition, yet their reliability and long-term efficiency are constantly threatened by hidden defects and progressive degradation. Early and precise identification of such anomalies is essential for ensuring safety, enhancing performance, and facilitating predictive maintenance plans. Infrared thermography (IRT) is a non-invasive and cost-effective technique for the inspection of PV modules. This study proposes an efficient attentive U-Net architecture for the semantic segmentation of thermographic images, aimed at supporting predictive maintenance and power loss assessment. The model integrates squeeze-and-excitation (SE) and attention gate (AG) modules with atrous spatial pyramid pooling (ASPP), achieving an optimal balance between accuracy and computational complexity. A comprehensive ablation study, including input resolution and module combinations, was conducted on a dataset of 500 thermograms annotated into six defect classes. The proposed configuration (256 × 256 input) achieved a mean Intersection over Union (mIoU) of 81.4% and a macro-F1 score of 87.5%, outperforming U-Net and DeepLabv3+ by over 4 percentage points, with only 5.24 M parameters and an inference time of 118.6 ms per image. These results confirm the suitability of the framework for energy-oriented fault diagnosis and near real-time monitoring of photovoltaic plants. Full article

To improve the detection performance of small defects in photovoltaic modules, we propose an enhanced YOLOv11n model—YOLO-FAD. Its core innovations include the following: (1) integrating RFAConv into the backbone network and neck network to better capture small defect features in complex backgrounds; (2) adding DyC3K2 for adaptive convolution optimization to improve accuracy and robustness; (3) employing ASF for multi-layer feature fusion, and combining it with DyHead-detect in the fourth detection layer to refine the classification and localization of small targets. Testing on our dataset shows that YOLO-FAD achieves an overall accuracy of 94.6% (85.3% for small defects), outperforming YOLOv11n by 3.0% and 10.1% in mAP, respectively, and surpassing YOLOv12, RT-DETR, Improved Faster-RCNN, and state-of-the-art (SOTA) improved models. Full article

Photovoltaic (PV) panel defect detection is essential for maintaining power generation efficiency and ensuring the safe operation of solar plants. Conventional detectors often suffer from low accuracy and limited adaptability to multi-scale defects. To address this issue, we propose YOLO-PV, an enhanced YOLO11n-based model incorporating three novel modules: the Enhanced Hybrid Multi-Scale Block (EHMSB), the Efficient Scale-Specific Attention Block (ESMSAB), and the ESMSAB-FPN for refined multi-scale feature fusion. YOLO-PV is evaluated on the PVEL-AD dataset and compared against representative detectors including YOLOv5n, YOLOv6n, YOLOv8n, YOLO11n, Faster R-CNN, and RT-DETR. Experimental results demonstrate that YOLO-PV achieves a 6.7% increase in Precision, a 2.9% improvement in mAP@0.5, and a 4.4% improvement in mAP@0.5:0.95, while maintaining real-time performance. These results highlight the effectiveness of the proposed modules in enhancing detection accuracy for PV defect inspection, providing a reliable and efficient solution for smart PV maintenance. Full article

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations. Full article

As the core component of photovoltaic (PV) power generation systems, PV cells are susceptible to subtle surface defects, including thick lines, cracks, and finger interruptions, primarily caused by stress and material brittleness during the manufacturing process. These defects substantially degrade energy conversion efficiency by inducing both optical and electrical losses, yet existing detection methods struggle to precisely identify and localize them. In addition, the complexity of background noise and other factors further increases the challenge of detecting these subtle defects. To address these challenges, we propose a novel PV Cell Surface Defect Detector (PSDD) that extracts subtle defects both within the backbone network and during feature fusion. In particular, we propose a plug-and-play Subtle Feature Refinement Module (SFRM) that integrates into the backbone to enhance fine-grained feature representation by rearranging local spatial features to the channel dimension, mitigating the loss of detail caused by downsampling. SFRM further employs a general attention mechanism to adaptively enhance key channels associated with subtle defects, improving the representation of fine defect features. In addition, we propose a Background Noise Suppression Block (BNSB) as a key component of the feature aggregation stage, which employs a dual-path strategy to fuse multiscale features, reducing background interference and improving defect saliency. Specifically, the first path uses a Background-Aware Module (BAM) to adaptively suppress noise and emphasize relevant features, while the second path adopts a residual structure to retain the original input features and prevent the loss of critical details. Experiments show that PSDD outperforms other methods, achieving the highest $mA{P}_{50}$ scores of 93.6% on the PVEL-AD. Full article

Stains, defects, and snow accumulation constitute three prevalent photovoltaic (PV) anomalies; each exhibits unique color and thermal signatures yet collectively curtail energy yield. Existing detectors typically sacrifice accuracy for speed, and none simultaneously classify all three fault types. To counter the identified limitations, an enhanced YOLOv11 framework is introduced. First, the hue-saturation-value (HSV) color model is employed to decouple hue and brightness, strengthening color feature extraction and cross-sensor generalization. Second, an outlook attention module integrated into the backbone precisely delineates micro-defect boundaries. Third, a mix structure block in the detection head encodes global context and fine-grained details to boost small object recognition. Additionally, the bounded sigmoid linear unit (B-SiLU) activation function optimizes gradient flow and feature discrimination through an improved nonlinear mapping, while the gradient-weighted class activation mapping (Grad-CAM) visualizations confirm selective attention to fault regions. Experimental results show that overall mean average precision (mAP) rises by 1.8%, with defect, stain, and snow accuracies improving by 2.2%, 3.3%, and 0.8%, respectively, offering a reliable solution for intelligent PV inspection and early fault detection. Full article

Surface defects of photovoltaic (PV) cells can seriously affect power generation efficiency. Accurately detecting such defects and handling them in a timely manner can effectively improve power generation efficiency. Aiming at the high-precision and real-time requirements for surface defect detection during the use of PV cells, this paper proposes a PV cell surface defect detection algorithm based on SEC-YOLOv8. The algorithm first replaces the Spatial Pyramid Pooling Fast module with the SPPELAN pooling module to reduce channel calculations between convolutions. Second, an ECA attention mechanism is added to enable the model to pay more attention to feature extraction in defect areas and avoid target detection interference from complex environments. Finally, the upsampling operator CARAFE is introduced in the Neck part to solve the problem of scale mismatch and enhance detection performance. Experimental results show that the improved model achieves a mean average precision (mAP@0.5) of 69.2% on the PV cell dataset, which is 2.6% higher than the original network, which is designed to achieve a superior balance between the competing demands of accuracy and computational efficiency for PV defect detection. Full article

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article

Solar photovoltaic/thermal (PV/T) systems can simultaneously solve PV overheating and obtain high-quality thermal energy through nanofluid spectral splitting technology. However, the existing nanofluid splitting devices have insufficient short-wavelength extinction and stability defects. To achieve the precise matching of the nanofluid splitting performance with the optimal spectral window of the PV/T system, this paper carries out a relevant study on the optical properties of Al nanoparticles and proposes an Al@Ag nanoparticle. The optical behaviors of nanoparticles and nanofluids are numerically analyzed using the finite-difference time-domain (FDTD) method and the Beer–Lambert law. The results demonstrate that adjusting particle size enables modulation of nanoparticle extinction performance, including extinction intensity and resonance peak range. The Al@Ag core–shell structure effectively mitigates the oxidation susceptibility of pure Al nanoparticles. Furthermore, coating Al nanoparticles with an Ag shell significantly enhances their extinction efficiency in the short-wavelength range (350–640 nm). After dispersing Al nanoparticles into water to form a nanofluid, the transmittance in the short-wavelength range is significantly reduced compared to pure water. Compared to 50 nm pure Al particles, the Al@Ag nanofluid further reduces the transmittance by up to 13% in the wavelength range of 350–650 nm, while having almost no impact on the transmittance in the photovoltaic window (640–1080 nm). Full article

Photovoltaic (PV) systems are at the heart of the energy transition, providing an essential source of clean, renewable energy for applications such as solar pumping, which is essential for irrigation and rural water supply. However, their efficiency depends directly on the quality and performance of the modules, which are often affected by defects or unfavorable environmental conditions. This article presents the development of an innovative automated tool designed for advanced characterization of PV modules by analyzing key parameters such as voltage and current. The system integrates measurement sensors (voltage, current, temperature, etc.), an Arduino Mega board and an SD card, enabling real-time data collection, processing, and recording under various environmental conditions. The results of the experimental tests demonstrate a significant improvement in the PV panel selection process, ensuring optimized choices at the time of purchase and rigorous monitoring during operation. This innovation contributes to maximizing energy performance and extending panel longevity, reinforcing their role in the transition to a sustainable energy model. Full article