Thin-film photovoltaic modules are often assumed to exhibit a linear reduction in output power when subjected to partial shading. However, this practice lacks a firm foundation in the literature. We experimentally demonstrate the linear response to partial shading using indoor flash testing of a commercial First Solar Series 6 thin-film module. We also demonstrate that a simple linear irradiance adjustment results in accurate energy yield simulations using data from a utility-scale thin-film photovoltaic system. Finally, to facilitate future work in this area, we document our simple and efficient method of measuring the impact of partial shading in a flash tester using a semitransparent material mimicking shading conditions in the field.

Modern utility-scale photovoltaic (PV) systems consist of hundreds of thousands of modules interconnected by low-cost connectors to create a DC network spanning hundreds of acres. PV connector failure is increasingly being identified as a significant cause of system outages, leading to generation losses, repair costs and capital losses due to fires. Anecdotally, connector failures have frequently been dismissed as being due to "counterfeits" or intermating between incompatible brands. In 2022, we launched a campaign to engage with utility owners, operators and field crews to better understand the source of PV connector failures and collect statistically relevant samples for laboratory analysis. Our findings to date challenge the preconceived sources and instead point to assembly and installation errors, materials defects and even internal mechanical design. To better understand the severity of these sources, we are developing an outdoor test capability to reproduce observed errors and stressors in a controlled fashion and monitor degradation and failure in-situ. Failing components will be evaluated to confirm equivalency with field observed failures. The outdoor test capability will be operated in at least two climates, including hot-dry and hot-humid. This paper describes the design of the outdoor test capability and planned test conditions.

Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current-voltage (I-V) curve analyzes for identification of damage and power loss. I-V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I-V curves for PV modules from their corresponding EL images. The predicted I-V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I-sc, and open-circuit voltage V-oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R-s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R-sh and dark current I-o parameters are predicted with larger errors because of their sensitivity to small changes in the I-V curve.

We present outdoor observations of metal-halide perovskite modules deployed in the Photovoltaic Accelerator for Commercializing Technologies center, which houses one of the world's broadest efforts to test metal-halide perovskite photovoltaic modules outdoors. As of January 2025, outdoor testing has encompassed over 150 modules from 14 different partners. Our findings illustrate how daily changes in efficiency, driven by exposure to light, affect field performance in real-world conditions. These effects cannot be explained by existing outdoor performance models and frustrate the notion of a traditional temperature coefficient.

This work investigates several photovoltaic (PV) modules that have shown signs of metal contact corrosion due to field exposure in a hot and humid climate. This includes two multicrystalline silicon aluminum back surface field systems with 10 and 14 years of exposure and one monocrystalline silicon passivated emitter and rear cell system with four years of exposure. A comprehensive, multiscale characterization process is used to evaluate these PV modules in great detail. Current-voltage (I-V), Suns-V-OC measurements, electroluminescence imaging, infrared imaging, and ultraviolet fluorescence imaging were performed, and locations of interest were cored and analyzed using cross-sectional scanning electron microscopy (SEM). A rigorous, quantitative analysis procedure for the cross-sectional SEM images is proposed and implemented. Careful characterization does reveal that some of these PV modules do indeed exhibit the same classic signs of acetic-acid-based corrosion of the glass frit that is present at the silver/silicon interface, which have been observed previously in PV modules exposed to damp heat in an environmental chamber.

Photovoltaic (PV) connectors, which link modules in series and connect PV strings in parallel, have increasingly been recognized as a primary contributor to PV system failures and a source of numerous fire incidents. However, publicly available data on the rates and types of connector failures are scarce, primarily due to the proprietary nature of the information and the need for comprehensive analysis. This study represents the first large-scale investigation of harvested PV connectors, drawing from a dataset of 6276 connectors from residential rooftop solar systems across the United States. The outcome of this work is twofold: 1) we have established a rapid characterization method for large populations of harvested connectors, incorporating visual inspection, resistance measurements, and X-ray imaging; and 2) the analysis made possible by our rapid-processing method has revealed, for a population of connector models provided by a single rooftop installer, failure statistics and insights for various connector makes and models, installation practices, operating currents, and internal component displacements. This research identifies common failure modes that could be considered in future connector designs standards, and operations and maintenance practices, to ultimately improve the reliability of this vital component of PV infrastructure.

Photovoltaic (PV) systems rely on discrete connectors for the efficient and safe flow of power from module to module and from strings to combiner boxes and inverters. Despite their functional importance, no common nomenclature for PV connectors currently exists, resulting in confusion and miscommunication. Misunderstood terms like "MC4 compatible", "cross-mating", "intermating", "female", and "male" can lead to installation and maintenance errors and compromise system reliability. We believe a standardized terminology will reduce confusion, help support installation best practices, aid in maintenance and repair, inform next-generation designs, and provide a technical basis for improved codes and standards. To that end, we are proposing a standardized glossary for 4 mm PV connectors (the most common type of connector used in PV applications) based on, and validated by, a Sandia National Laboratories' investigation that included the following sources: 1) a comprehensive review of official documents from 20 connector manufacturers, including schematics, datasheets, installation manuals, and catalogs, as well as relevant patents; 2) two rounds of surveys distributed to stakeholders, including connector manufacturers, engineers, asset owners, test labs, and researchers; and 3) visual examination of 25 different models of 4 mm single-pole DC PV connectors to document variations in design and functionality. This work provides a foundation for establishing a clear and consistent terminology for PV connectors that will in turn enable progress toward greater reliability and collaboration across the industry.

We present outdoor observations on metal halide perovskite modules deployed in the Perovskite Photovoltaic Accelerator for Commercializing Technologies (PACT) Center which represents the world’ broadest effort to test perovskite photovoltaic modules outdoors. Outdoor testing to date has encompassed over 100 modules from nine different partners. Our findings illustrate how daily changes in efficiency, driven by exposure to light, affect field performance in real-world conditions. These effects cannot be explained by existing outdoor performance models and frustrate the notion of a traditional temperature coefficient. We also present observations about the long-term performance of the modules, including symptoms of degradation as they manifest in the field.

The solar energy industry is rapidly expanding and constantly modifying design, and bill of materials. The prolific advancement of photovoltaic (PV) technology only emphasizes the importance of field research for ensuring reliability and validating the accelerated aging methodologies that allow researchers to avoid waiting 20 - 30 years for the natural failure of the device. In this research, we take a multiscale analytical approach to investigate fielded PV modules that were showing signs of contact corrosion. To assess module-level performance, we conducted current-voltage (IV) and Suns-VOC measurements, electrolumi-nescence (EL) imaging, infrared (IR) imaging, and ultraviolet fluorescence (UVF) photography imaging on multicrystalline silicon (multi-Si) and monocrystalline silicon (mono-Si) modules which operated in a hot and humid climate. Additionally, we demonstrate a method for quantitative characterization of PV front contacts with image processing of SEM cross-sectional images.

We report on an updated field trial to test measurement of diffuse horizontal irradiance (DHI) using a novel low-cost combination of static sensors with no moving parts. The system pairs two tilted reference cells, one of which measures global irradiance and the other of which is modified with an isolator tube that admits only diffuse light contributions from a limited region of the sky. Using reference data from a research-grade tracking diffusometer, we trained a neural network model to use the readings of the two static sensors to estimate DHI. In this work, we have updated our field testing to include almost 1 year of data from the test site. A second test site at different latitude was recently installed and will be used for additional validation in subsequent work.

System health monitoring is an essential task in the operation and maintenance of any photovoltaic (PV) system. Typically, electroluminescence (EL), thermal imaging, and current-voltage (IV) curve analyses are used to analyze PV modules with each providing unique insights into system health. While it is relatively easy to acquire an EL or thermal image of a panel in-situ, acquisition of IV curves requires electrical disconnection of the panel from the array and either removal to a solar simulator or characterization and correction for the incident solar spectrum and intensity. In this work we show that, with the use of a transfer-learned Swin transformer model, we can predict accurate IV curves from EL images. Extracting single diode equation parameters from the predicted IV curves yielded an error less than 1%+/- 1% for the maximum power point (MPP), short-circuit current I-sc, open-circuit voltage V-oc and photocurrent I. The series resistance Rs and number of series cells nN(s)V(th) denoted as N were predicted with errors of similar to 5%+/- 7% and similar to 3%+/- 2%, respectively. Prediction of the shunt resistance R-sh and dark current I-o yielded larger errors, likely due to sensitivity to small changes in the IV curve.

All freely available plane-of-array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib-python and pvpltools-python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853-1 and 61853-2 testing, and the input data for each model were sourced from these system-specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within +/- 3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from -6.5 degrees C to 2.7 degrees C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within +/- 4.2% and an overall average NMBE within +/- 2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system-specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system-specific data.

We report on plans for a field trial now in progress to test measurement of global horizontal irradiance (GHI), direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and reflected horizontal irradiance (RHI) using an array of static sensors with no moving parts. As in our recent work in this area, the collection of static sensors includes reference cells in multiple orientations. In addition, our current system under test includes a modified reference cell with a collimation tube to admit only diffuse light contributions from a limited region of the sky. We are developing an analysis model to determine GHI, DNI, DHI, and RHI from the combined sensor data. Field trials have recently begun. Results will be published at a later date.

Aging of silicon photovoltaic (PV) module packaging is one of the greatest limiters of PV module service lifetimes. Module characterization typically focuses on power degradation metrics, which do not convey the complexities of often simultaneous degradation mechanisms. In this work, PV modules with pristine references and known fielding histories were investigated by non-destructive and destructive methods. Modules from Canadian Solar, Mission Solar, and Hanwha Q-Cells were fielded for up to three years; select modules were removed from fielding each year for coring to allow for characterization of the encapsulant. Modules are commonly encapsulated with two protective layers of partially-crystalline ethylene vinyl acetate (EVA) polymer that must undergo a crosslinking reaction to achieve desired properties. The extent of crystallinity of the encapsulants as studied by differential scanning calorimetry showed differences between manufacturers and over time. Some encapsulants showed different magnitudes of crystal sizes which changed after fielding; encapsulants with the monodisperse crystal sizes did not change with fielding. This is due to differences in thermal history. These results have implications for stress development during module aging, since EVA crystal melting and crosslinking reactions can result in encapsulant density changes.

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.

All freely available plane-of-array (POA) transposition models, photovoltaic (PV) module/cell temperature models, and PV performance models were examined against multi-year field data from Albuquerque, New Mexico. The data include different PV systems comprised of c-Si modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853 testing and the input data for each model were sourced from these test results. Six POA transposition models, seven temperature models, and twelve performance models are included in this comparative analysis. These freely available models were proven effective across many different types of c-Si technologies. Overall, it was observed that model complexity and/or availability of module-specific characterization data does not guarantee greater accuracy, at least in Albuquerque. The POA transposition and PV performance models exhibited average normalized mean bias errors (NMBE) within ±3 %; the mean and median residuals of the PV temperature models were within ±5°C.

The cost of photovoltaic (PV) modules has declined by 85% since 2010. To achieve this reduction, manufacturers altered module designs and bill of materials; changes that could affect module durability and reliability. To determine if these changes have affected module durability, we measured the performance degradation of 834 fielded PV modules representing 13 module types from 7 manufacturers in 3 climates over 5 years. Degradation rates (Rd) are highly nonlinear over time, and seasonal variations are present in some module types. Mean and median degradation rate values of -0.62%/year and -0.58%/year, respectively, are consistent with rates measured for older modules. Of the 23 systems studied, 6 have degradation rates that will exceed the warranty limits in the future, whereas 13 systems demonstrate the potential of achieving lifetimes beyond 30 years, assuming Rd trends have stabilized.

Recent interest within the photovoltaic (PV) module industry is largely directed toward enhanced lifetimes in the field, balanced with improved recyclability. Traditionally, fluoropolymer-based backsheets have been used, however, are difficult to recycle. Emerging polyolefin (PO)-based backsheets are more recyclable and can be formulated to be robust. Properties of different fluoropolymer- and non-fluoropolymer-based backsheet coupons and in encapsulated silicon mini modules that have been fielded in Albuquerque, NM and Cocoa, FL are reported here. Seven backsheets were examined: two novel PO's, TPT, APO, PPE, AAA, and KPf. Methods of examination include module electrical performance (I - V flash test), surface morphology (optical microscope and gloss), polymer chemical structure (FTIR), EL imaging, mechanical tensile testing, DC breakdown voltage, DSC (phase transitions), and optical performance (reflectance spectra).