Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images

Fioresi, Joseph; Colvin, Dylan J.; Frota, Rafaela; Gupta, Rohit; Li, Mengjie; Seigneur, Hubert; Vyas, Shruti; Oliveira, Sofia Ares; Shah, Mubarak; Davis, Kristopher O.

doi:10.1109/jphotov.2021.3131059

Cited by 39 publications

(4 citation statements)

References 53 publications

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“…Various researchers in the literature have deployed several material characterization techniques such as SEM, EDS, and top-down high-resolution X-ray photoelectron spectroscopy (XPS) analytical processes and imaging techniques on cored samples to analyze corrosion [40,[76][77][78][79]. In [78,80], the authors used EL imaging as characterization method to analyze degradation signatures including corrosions in both multicrystalline and monocrystalline silicon cells. The authors in [78] used ac impedance spectroscopy to analyze degradation in crystalline silicon modules that are exposed to HAc vapor.…”

Section: Corrosionmentioning

confidence: 99%

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

2022

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The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies.

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Section: Corrosionmentioning

confidence: 99%

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

2022

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“…There are three main steps: spraying an epoxy coating mixed with ZnS, depositing a Cu electroluminescent powder on the insulator surface, obtaining photoluminescence images under different voltages, and determining the electric field distribution via the proposed image processing algorithm. Fioresi et al [11] proposed a deep learning-based semantic segmentation model that identifies and segments defects in EL images of silicon PV cells, which utilizes a segmentation Deeplabv3 model with a ResNet50 backbone.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Defective Solar Panels in Electroluminescence Images Based on Random Connection Network

Xu,

Shi,

Yang

et al. 2024

Electronics

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Solar energy is an important renewable energy source, and the efficiency of solar panels is crucial. However, tiny cracks and dark spots, defects of panels, can significantly affect power generation performance. To solve the defect identification problem of solar panels, an intelligent electroluminescence (EL) image classification method based on a random network (RandomNet50) is proposed. The randomly connected network module is designed by combining dropout and feature reuse strategies. Feature reuse in random networks optimizes the network structure and improves the feature utilization efficiency. The network model uses geometric transformation and the deep convolution generative adversarial network (DCGAN) method to enhance few-shot EL images (400) with different states. The comparison experiment shows that the RandomNet50 has a good classification effect on the enhanced images. The accuracy of the CIFAR-10/EL dataset (96.15%/88.23%) is better than the residual and dense networks. The method has high classification accuracy and provides strong technical support in the field of solar cells.

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“…In silicon wafer and solar cell fields, many studies have investigated micro-crack defect detection methods, which can be generally divided into the following categories according to their respective technical principles [3,4]: light beam induced current [5], lock-in thermography [6], resonance ultrasonic vibration [7], scanning acoustic microscopy [8], optical transmission [9], photoluminescence (PL) [10], and electroluminescence (EL) [11]. However, these methods are primarily used to detect micro-crack defects on the surface or internal structure of silicon wafers and solar cells.…”

Section: Introductionmentioning

confidence: 99%

A Gabor filter based weak crack detection method for glassivation passivation parts wafer

Lang,

Zheng,

et al. 2023

Meas. Sci. Technol.

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Crack defects on the surface of glassivation passivation parts (GPP) wafers are a significant factor leading to chip functional failure. Thus, efficient and stable surface defect detection is required to ensure product quality. However, existing detection methods do not handle weak crack defects on the wafer surface with an inhomogeneous texture background effectively. To overcome this challenge, we propose a weak crack defect detection method for GPP wafers with complex backgrounds. First, a weak crack feature enhancement method is proposed based on the Gabor filter with different orientations. Then, the crack candidates are generated using the least-squares method to transform the shape-based information into a line segment and direction independence judgment. Finally, a line segment clustering method is implemented for the final crack defect recognition. Experimental results demonstrate that the proposed defect detection method works effectively and robustly with inhomogeneous texture backgrounds and satisfies the requirement of a real-time detection system.

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Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images

Cited by 39 publications

References 53 publications

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

Automatic Classification of Defective Solar Panels in Electroluminescence Images Based on Random Connection Network

A Gabor filter based weak crack detection method for glassivation passivation parts wafer

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