From Modules to Atoms: Techniques and Characterization for Identifying and Understanding Device-Level Photovoltaic Degradation Mechanisms

Johnston, Steven W.; Moutinho, H. R.; Jiang, C.-S.; Guthrey, Harvey; Norman, A. G.; Harvey, Stephen B.; Hacke, Peter; Xiao, Chuanxiao; Moseley, John; Sulas, Dana; Liu, Jun; Albin, David S.; Nardone, Marco; Al‐Jassim, Mowafak

doi:10.2172/1572277

Cited by 7 publications

(4 citation statements)

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“…Nondestructive imaging can further guide targeted module-to-microscopic structural and chemical characterization of defects with a goal of building mechanistic understanding behind observed degradation modes. [56,64] Our future work aims to implement similar concepts to develop predictive accelerated stress testing protocols supported by electro-optical imaging and eventually targeted microscopic analysis of defects. [65]…”

Section: Discussionmentioning

confidence: 99%

“…We measured DLIT using a Cedip Silver SC5600 camera with a cooled InSb sensor having sensitivity in the 3–5 μm range, interfaced with Altair software. [ 64 ] For lock‐in acquisition, we pulsed constant current through the mini‐modules and synced the camera to the same reference frequency. We did this using a square wave from a Tektronix AFG3102 function generator that triggers both a Keithley 2401 source meter and the camera at 0.5 Hz.…”

Section: Methodsmentioning

confidence: 99%

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Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Schall,

Glaws,

Doumon

et al. 2023

Solar RRL

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Herein, electroluminescence (EL) and thermal imaging are used to examine p-i-n metal halide perovskite (MHP) photovoltaic (PV) mini-modules (MA 0.6 FA 0.4 PbI 3 , 20 cells, 78 cm 2 ) before and after indoor-accelerated stress testing or outdoor deployment. Distinct spatial patterns in the EL images emerge, which depend on the external stress conditions experienced by the mini-module. Imaging results highlight a distribution of dark speckle features that dominate after UV stress, attributed to widespread interfacial contact degradation. Lateral intensity gradients across cells dominate after thermal cycling (TC) stress, attributed to current crowding near scribe defects. While current-voltage analysis alone does not give full insight on the degradation process, this study shows that distinct degradation modes can be further defined by multimodal electro-optical imaging (i.e., EL combined with photoluminescence and dark lock-in thermography). Neither UV exposure nor TC-accelerated stress testing alone replicates the same degradation signatures observed after outdoor deployment, suggesting that multiple degradation modes occur under concurrent stressors outdoors. Spatial characterization of degradation modes in MHP PV mini-modules before and after accelerated stress testing lays the groundwork for developing targeted accelerated stress testing procedures through comparison with outdoor aging.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Schall,

Glaws,

Doumon

et al. 2023

Solar RRL

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“…In order to mitigate PID, operators typically install anti-PID box between the strings and the inverter. PID has received considerable attention in recent years due to its detrimental impact on both crystalline silicon (PID-shunting) and thin-film PV module performance under field conditions [92]. The authors in [19] investigated the correlation between power losses and the performance ratio of modules that are having PID challenges.…”

Section: Potential Induced Degradation (Pid)mentioning

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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“…The most relevant method to overcome this issue is therefore to extract samples from commercial modules exposed in the field ('coring') and prepare them to be analysed in the lab. A similar extraction method has been reported in the past and applied to study local features, for example, by electroluminescence and electron backscatter diffraction, 10,11 also on c-Si. 12 Here, we go a step further and demonstrate that the coring and, more importantly, the unpackaging stage, can be carried out without any detectable damage to the active layers, yielding fully functional PV devices.…”

Section: Introductionmentioning

confidence: 99%

Extraction and microscopic analysis of partial shading‐induced defects in a commercial CIGS PV module

Aninat¹,

Bakker²,

Jouard³

et al. 2022

Progress in Photovoltaics

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The ever‐increasing instalment capacity of Cu (In, Ga)(Se, S)2‐based photovoltaics calls for a better understanding and control of their reliability. In this paper, we show how using a coring‐based method, small samples can be extracted from full size commercial modules, and prepared for lab‐scale analysis. The method is applied to a Cu (In, Ga)(Se, S)2 (CIGS) module where a non‐reversible, propagating (‘wormlike’) defect has been created in a controlled partial shading experiment. Through current–voltage, photoluminescence and illuminated lock‐in thermography analyses on an undamaged part of the module, the method used is shown to yield fully functional, undamaged active cells, with a photovoltaic conversion efficiency above the full module efficiency. Where the wormlike defects were present, a typical strong shunting behaviour is observed, as well as an increased sulphur content near the edge of the wormtrails. Furthermore, the wormlike defect propagation is shown to be strongly influenced by the present of specific features near the interconnects, which could be the result of manufacturing. These results demonstrate the potential of coring to analyse module failure with all the laboratory tools available. They also shed some light on how wormlike defects, which are a rare but serious hazard for CIGS modules reliability, can form and propagate in commercial, monolithically interconnected modules.

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From Modules to Atoms: Techniques and Characterization for Identifying and Understanding Device-Level Photovoltaic Degradation Mechanisms

Abstract: Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Cited by 7 publications

References 50 publications

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

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

Extraction and microscopic analysis of partial shading‐induced defects in a commercial CIGS PV module

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