Every year Kiwa PVEL’s Photovoltaic (PV) Module Reliability Scorecard highlights the technical advancements and limitations of commercially available solar modules. Modules included in the Scorecard are subjected to a series of extended reliability and performance tests, known as the Product Qualification Program (PQP), and the aggregated data and analysis is published at scorecard.pvel.com. This has become the industry’s leading publication on PV module reliability and offers readers a wide range of key insights. In June 2025, Kiwa PVEL will release the 11th edition of this annual report. While the Scorecard often showcases the advances made in PV manufacturing, it also reveals concerning trends. For example, the 2024 Scorecard reported that two-thirds of participating manufacturers and 41% of bill of materials (BOMs) experienced at least one test failure. Specific PQP tests, like thermal cycling (TC) and damp heat (DH), showed significant failure rates. This paper will offer a deep dive into the latest findings from the 2025 Scorecard, which promise to be as impactful as those included in previous editions.

Modern solar PV technologies like Tunnel Oxide Passivated Contact (TOPCon) have shown notable power degradation in specific bills of materials (BOMs) under accelerated ultraviolet light-induced degradation (UVID) testing. Initial testing revealed a wide range of power degradation (-0.8% to -16%) after UV dose of 120 kWh/m2 (280-400 nm) using metal halide lamps. The power loss due to UVID is mainly attributed to Voc degradation (passivation loss or reduction in carrier lifetime). A few BOMs also exhibited higher Isc losses, whereas the contribution of FF is small. With more data collection, it became evident that the high degradation observed in some modules was not statistically significant. UVID is not proving to be as severe as initially anticipated. Notably, the characteristic UVID signatures in EL images are observed in field-deployed modules, supporting the correlation between laboratory results and real-world performance. Another critical challenge associated with UV-sensitive modules is their metastable behavior, with pronounced sensitivity to dark conditions as indicated by remarkable power decline within a single day of dark storage, underscoring the necessity of precise timing in module characterization post UVID or field exposure. Furthermore, we have explored potential stabilization techniques, such as indoor/outdoor light soaking, current injection, and UV-365 nm exposure) and plan to contribute these findings to the development of updated IEC standards.

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model's decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

Solar modules in utility-scale photovoltaic (PV) systems are expected to have a prolonged lifetime to effectively compete with traditional energy sources. However, their durability is often compromised by cyclic thermomechanical stress, highlighting the importance of optimized module design to mitigate thermal expansion incompatibility. The complex composition of solar modules makes isolating the impact of individual components on overall durability challenging. To address this, we have compiled a comprehensive dataset, encompassing bill-of-materials and post-thermal cycling power loss for over 250 unique module designs. Using this dataset, we developed machine learning(ML) models to correlate design factors with degradation. We employed the Shapley additive explanation method to interpret the model's predictions, focusing on the influence of various design factors on module durability. Our analysis reveals that the type of silicon in solar cells, particularly monocrystalline versus polycrystalline, significantly affects degradation, with mono crystalline cells showing enhanced durability. We also found that encapsulant thickness is another vital factor. While thicker encapsulants lead to less power loss, additional thickness beyond a certain point does not yield further benefits. In addition, we further substantiated the ML interpretation by statistical testing. This study provides a novel approach for employing explainable machine learning in complex material systems and offers insights that could guide future research in optimizing solar module design, enhancing both the durability and efficiency of PV systems.

Modern cell technologies are found to be prone to UV-induced degradation (UVID), impacting their electrical performance, reliability and warranty considerations in the field. This paper presents the findings of Kiwa PVEL's UVID test conducted on industrial modules, including n-type TOPCON, HJT, and p-type PERC sourced from manufacturers worldwide. The modules under short-circuit condition were subjected to front-side UV exposure to a cumulative UV dose (280-400 nm) of 120 kWh/m(2) using metal halide lamps in a chamber at 60 degrees C. Each module underwent a comprehensive characterization process involving visual inspection, I-V measurements at STC and low irradiance 200 W/m(2), EL imaging at varying current injection levels, and wet leakage current testing. The results reveal varied UVID sensitivity across different cell types and manufacturers, suggesting that degradation is not solely determined by cell architecture, but is significantly influenced by the BOM and manufacturing processes employed. The highest degradation observed in TOPCon, HJT, and PERC modules was -9.7%, -6.7%, and -2.8% respectively.

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current-voltage characteristic (I -V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I -V curves of the PV system level are often not available. This paper proposes a meth-odology (PVPRO) to estimate these I -V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.

The effect of cracks in solar cells on the long-term degradation of photovoltaic (PV) modules remains to be determined. To investigate this effect in future studies, it is necessary to quantitatively describe the crack features (e.g., length) and correlate them with module power loss. Electroluminescence (EL) imaging is a common technique for identifying cracks. However, it is currently challenging and time-consuming to identify cracks in a large number of EL images and quantify complex crack features by human inspection. This article introduces a fast semantic segmentation method ($\sim$0.18 s/cell) to automatically segment cracks from EL images and algorithms to extract crack features. We fine-tuned a UNet neural network model using pretrained VGG16 as the encoder and obtained an average F1 score of 0.875 and an intersection over union score of 0.782 on the testing set. With cracks and busbars segmented, we developed algorithms for extracting crack features, including the crack-isolated area, the brightness inside the isolated area, and the crack length. We also developed an automatic preprocessing tool for cropping individual cell images from EL images of PV modules ($\sim$0.72 s/module). Our codes are published as open-source an software, and our annotated dataset composed of various types of cells is published as a benchmark for crack segmentation in EL images.

Cracks on solar cells can cause degradation of photovoltaic (PV) modules, and electroluminescence (EL) images are a common technique for identifying cracks. However, to process a large number of such images it is necessary to develop automated routines for analysis. This article introduces a fast semantic segmentation method (~0.18s/cell) to segment crack lines, cross cracks, busbars, and dark areas on EL images of PV modules. We trained a UN et neural network model on a training set of 1,272 images, and we evaluated its performance on a validation set of 206 images and a testing set of 359 images. We report the performance on the testing set with an average F1 score of 0.875 and an IoU score of 0.782. We introduce our algorithm of predicting the worst-case degradation area with cracks detected. We also demonstrate our automatic preprocessing tool of cropping individual cell images from EL images of PV modules (~0. 72s/module). Our methods are published as open-source software and might be used to segment other kinds of defects or similar types of images by transfer learning in the future.

Physics-based circuit parameters like series and shunt resistance are also essential to provide insights into the degradation modes of PV arrays. However, the calculation of these parameters typically relies on I-V curves, which require specific measurement devices and may impede the regular operation of PV systems. Although a reference module equipped with I-V tracers may be installed next to the array, the recorded I-V curves cannot fully represent the array condition. Therefore, I-V curves of the entire array are not always available to obtain the circuit parameters, especially for large-scale PV power plants. Therefore, this paper proposes a methodology (PVPRO) to estimate these parameters for degradation analysis without the need for I-V tracers. Rather, it estimates these parameters using only operation (DC voltage and current) and weather data (irradiance and module temperature). PVPRO first performs a multi-stage data cleaning and filtering. Next, the time-series DC data are clipped into windows to fit the equivalent single-diode model to estimate the circuit parameters on minimizing the loss between real and estimated values. Finally, the estimated parameters are plotted as function of time to quantitatively analyze the degradation trends. PVPRO is evaluated on synthetic datasets in the presence of noise and is applied to a well-qualified field system (2015-2019). The degradation trend by PVPRO is in reasonable agreement with the ground truth, especially for the seasonal variation (correlation coefficient of 0.71). Future work will apply PVPRO to more large-scale PV systems and deconvolve the degradation pathways. The current version of PVPRO is available on Github: https://github.com/DuraMAT/pvpro

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. We develop a rapid automatic computer vision pipeline (∼0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

Commercial antireflective coatings (ARCs) on photovoltaic (PV) module glass can improve module power by 2.5%–3.0%, but their long‐term field performance requires additional study. In this paper, we investigate ARC performance on fielded modules using two nondestructive techniques: reflectance spectroscopy and RGB microscopy, finding a large variation in coating durability and performance. For new coatings, the fleet average nominal power enhancement is 2.8%. This power enhancement is observed to degrade at approximately −0.05%/year absolute over the first 8 years of fielding in arrays that are not regularly cleaned. Interferometry and imaging results imply that coating loss for these modules is due to slow chemical thinning of the initial 125 nm thick coating by 1.4 to 5.0 nm/year. Due to the physics of interference coatings, the performance loss is projected to accelerate as the coating is further thinned, resulting in a coating lifetime (time to 80% of initial performance) of 7.5 to 25 years. At the higher 5.0 nm/year coating loss rates, we estimate that −0.14%/year power degradation of the module can be attributed solely to ARC degradation over the first 20 years. Extreme coating loss is observed on some modules where after 8 years fielding, the coating can be completely removed with a single wet wipe with a lens tissue. A case study is also presented comparing ARC and noncoated modules installed at a single site in 2013. We find that the ARC modules have a 2.6% higher soiling power loss than the noncoated modules; this exceeds the ARC power enhancement of 2.4% and leads to the surprising conclusion that the ARC lowers production for modules in this location due to increased soiling. These results demonstrate that RGB microscopy is a powerful, field‐capable, and quantitative characterization technique for assessing degradation of PV module ARCs.

Antireflective coatings (ARCs) are used on the vast majority of solar photovoltaic (PV) modules to increase power production. However, ARC longevity can vary from less than 1 to over 15 years depending on coating quality and deployment conditions. A technique that can quantify ARC degradation nondestructively on commercial modules would be useful both for in-field diagnostics and accelerated aging tests. In this article, we demonstrate that accurate measurements of ARC spectral reflectance can be performed using a modified commercially available integrating-sphere probe. The measurement is fast, accurate, nondestructive, and can be performed outdoors in full-sun conditions. We develop an interferometric model that estimates coating porosity, thickness, and fractional area coverage from the measured reflectance spectrum for a uniform single-layer coating. We demonstrate the measurement outdoors on an active PV installation, identify the presence of an ARC, and estimate the properties of the coating.

Hole spins in semiconductors are a potential qubit alternative to electron spins. In nuclear-spin-rich host crystals like GaAs, the hyperfine interaction of hole spins with nuclei is considerably weaker than that for electrons, leading to potentially longer dephasing times. Here we demonstrate optical pumping and coherent population trapping for acceptor-bound holes in a strained GaAs epitaxial layer. We find mu s-scale longitudinal spin relaxation time T-1 and an inhomogeneous dephasing time T-2* 2 of similar to 7 ns. We attribute the spin relaxation mechanism to the combined effect of a hole-phonon interaction through the deformation potentials, and heavy-hole-light-hole mixing in an in-plane magnetic field. We attribute the short T-2* to g-factor broadening due to strain inhomogeneity. T-1 and T-2* are calculated based on these mechanisms and compared with the experimental results. While the hyperfine-mediated decoherence is mitigated, our results highlight the important contribution of strain to relaxation and dephasing of acceptor-bound hole spins.

Novel methods for advancing reliability testing of photovoltaic (PV) modules and materials have recently been developed. Combined-accelerated stress testing (C-AST) is one such method which has demonstrated reliable reproduction of some field-failures which were not reproducible by standard certification tests. To increase confidence and assist in the development of C-AST, and other new testing protocols, it is important to validate that the failure modes observed and mechanisms induced are representative of those observed in the field, and not the product of unrealistic stress conditions. Here we outline a method using appropriate materials characterization and modelling to validate the failure mechanisms induced in C-AST such that we can increase confidence in the test protocol. The method is demonstrated by applying it to a known cracking failure of a specific polyamide (PA)-based backsheet material. We found that the failure of the PA-based backsheet was a result of a combination of stress factors. Photo-oxidation from ultra-violet (UV) radiation exposure caused a reduction in fracture toughness, which ultimately lead to the cracking failure. We show that the chemical and structural changes observed in the backsheet following C-AST aging were also observed in field-aged samples. These results increase confidence that the conditions applied in C-AST are representative of the field and demonstrates our approach to validating the failure mechanisms induced.