Review: Ultraviolet Fluorescence as Assessment Tool for Photovoltaic Modules

Köntges, Marc; Morlier, Arnaud; Eder, Gabriele; Fleis, Eckhard; Kubicek, Bernhard; Lin, Jay

doi:10.1109/jphotov.2019.2961781

Cited by 74 publications

(82 citation statements)

References 39 publications

(52 reference statements)

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“…This is because of photo-bleaching over the edges or the regions between cells due to the diffusion of oxygen into the module ( Fig 5 ). Similar effect has been reported in the literature previously [ 29 , 32 , 33 , 37 , 38 ].…”

Section: Resultssupporting

confidence: 92%

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Assessing the reliability and degradation of 10–35 years field-aged PV modules

Noman

Ahmad

et al. 2022

PLoS ONE

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The objective of this study was to conduct a reliability analysis on photovoltaic (PV) modules from the oldest PV installation site in Pakistan. Four sets of modules; Type A & B (30 years old), Type C (10 years old), and Type D (35 years old) were identified for this analysis. It has been observed that modules have shown degradation after working for a good number of years in the field. Comparing with nameplate data (available for Type B & C only), a drop of 28.68% and 2.99 percentage points (pp) was observed in the output power (Pmax) and efficiency (Eff.) respectively for Type B, while a drop of 22.21% and 4.05 pp was observed in Pmax and Eff. respectively for Type C. A greater drop in ISC and Pmax was observed in Type B, which is attributed to severe browning of EVA in them. While the greater drop in Pmax, in case of Type C, is attributed to the poor quality of materials used. Amongst the different defects observed, the junction box defects which include cracking and embrittlement, etc., and backsheet defects which include discoloration, delamination and cracking, etc. were found in all four types of modules. Other defects include browning of EVA, observed in Type B and D, and corrosion of frame and electrical wires, found in Type A, B, and D. This first-ever study will provide valuable information in understanding the degradation mechanism and henceforth, improving the long term reliability of PV modules in the humid-subtropical conditions of Pakistan.

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Section: Resultssupporting

confidence: 92%

“…Any difference in the intensity of the light emitted back can be used to analyze the defects. UV-F can also be used for detecting cracks in the cells and hot spots in the PV modules [ 29 – 33 ]. UV-F imaging of the present modules was performed in a dark room to remove any effect of visible light.…”

Section: Methodsmentioning

confidence: 99%

Assessing the reliability and degradation of 10–35 years field-aged PV modules

Noman

Ahmad

et al. 2022

PLoS ONE

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“…Namely, this analysis shows that differentiating between failure modes that show equivalent IV profiles and low power loss can be difficult. However, improving these classifications can serve as an important precursor to more serious failures, as in the case of cracked cell modules, which may allow oxygen and water vapor penetration, causing future reliability and power loss issues [31]. In certain cases, these misclassifications can be very dangerous.…”

Section: Resultsmentioning

confidence: 99%

Neural Network-Based Classification of String-Level IV Curves From Physically-Induced Failures of Photovoltaic Modules

et al. 2020

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Accurate diagnosis of failures is critical for meeting photovoltaic (PV) performance objectives and avoiding safety concerns. This analysis focuses on the classification of field-collected string-level current-voltage (IV) curves representing baseline, partial soiling, and cracked failure modes. Specifically, multiple neural network-based architectures (including convolutional and long short-term memory) are evaluated using domain-informed parameters across different portions of the IV curve and a range of irradiance thresholds. The analysis identified two models that were able to accurately classify the relatively small dataset (~400 samples) at a high accuracy (99%+). Findings also indicate optimal irradiance thresholds and opportunities for improvements in classification activities by focusing on portions of the IV curve. Such advancements are critical for expanding accurate classification of PV faults, especially for those with low power loss (e.g., cracked cells) or visibly similar IV curve profiles.

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“…The increasing fluorescence was caused by increasing concentration of fluorophores upon aging and originated from degradation products of the polymer and/or the additives within the polymer. A detailed review on this topic was recently published (see [32] and the references given therein).…”

Section: Measurements On Field-aged Pv Modulesmentioning

confidence: 99%

On-Site Identification of the Material Composition of PV Modules with Mobile Spectroscopic Devices

et al. 2020

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With the increased development of portable and handheld molecular spectrometers within recent years, new fields of applications have opened up, such as their use (i) for material identification of samples contained in large and non-portable components and (ii) the detection of material degradation effects and failures directly in the plant. The usability and transferability of well-established analytical characterization techniques, such as attenuated total reflection (ATR) Infrared (IR)-, Raman, and Near-Infrared (NIR)-spectroscopy as mobile devices for the in-field characterization of Photovoltaic (PV) modules, are described and discussed. Material identification of the polymeric compounds incorporated in the PV modules (encapsulants, backsheets) is often an important task, especially when degradation and failures occur. Whereas the knowledge of the bill of materials is one challenge, the detection of material degradation effects is another important issue. Both tasks can be solved nondestructively by the application of mobile spectrometers. Raman spectroscopy is the best-suited method for the identification of the encapsulant within the module (measurement through 3-mm glass), while NIR measurements allowed for the nondestructive determination of the composition of the multilayer backsheet. Surface degradation effects (e.g., oxidation, hydrolysis) are best detectable with IR-spectroscopy. The application of mobile devices allows for direct material analysis in the field without dismantling PV modules, transporting them to the lab, cutting them in smaller pieces, and analyzing them in conventional bench-top spectrometers.

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Review: Ultraviolet Fluorescence as Assessment Tool for Photovoltaic Modules

Cited by 74 publications

References 39 publications

Assessing the reliability and degradation of 10–35 years field-aged PV modules

Assessing the reliability and degradation of 10–35 years field-aged PV modules

Neural Network-Based Classification of String-Level IV Curves From Physically-Induced Failures of Photovoltaic Modules

On-Site Identification of the Material Composition of PV Modules with Mobile Spectroscopic Devices

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