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Raster-scanned laser beam excites photocurrents in thin-film amorphous silicon devices. Solar-cell laser scanner uses two galvanometer-driven mirrors to scan laser-beam spot over surface of module or cell under test. Position signals from scan controllers used to index storage of photocurrent signal data in scan-converter image memory. Stored image displayed on television monitor.
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. Here, 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.
A high efficiency configuration for a solar cell module comprises solar cells arranged in a shingled manner to form super cells, which may be arranged to efficiently use the area of the solar module, reduce series resistance, and increase module efficiency. Removing a defective solar cell from a super cell may be difficult, however. It may therefore be advantageous to bypass defective solar cells in a super cell rather than remove and replace them. A bypass conductor may be applied to the rear surface of the super cell to bypass one or more defective solar cells in a super cell or in a solar module comprising super cells.
Organometal halide perovskite solar cells have experienced eye-catching improvements in its recent few years. It serves as one of the most promising candidates to replace the currently widely used silicon-based solar modules. To implement its final step to the real application, functional longevity becomes the dernier continent to conquer. As a polycrystalline material, defect plays critical role in the efficiency and stability of the perovskite solar cells. Thus, the investigation of the defect physics of the perovskite layer is indispensable in this research field. However, hard evidence and a consensus are still lacking in terms of the specific nature of the defects and their effects on performance and hysteresis, and perhaps even more importantly, there is absence of fundamental understanding of the correlations between the defects and long-term operational stability of the device. A more fundamental understanding of the nature of defects in perovskite materials is of paramount importance to progress their efficiency and durability. In this work we propose in-depth studies of correlations of defects with performance and stability of perovskite solar cells. Our project aimed 1) to investigate the defects physics in perovskite solar cells, and 2) to develop a comprehensive understanding and physical model of defects and its influence on performance and stability of perovskite solar cells. With the support from program manager, Peter Lobaccaro, and the Solar Energy Technologies Office of U.S. Department of Energy, the project ends with impact achievements. Our research results have systematically provided strategies to analyze the influences of constructive molecular configurations to the charged defects in the perovskite lattices and developed in-depth understanding of chemical additive approach to improve the perovskite solar cell performance and stability. As history has shown us, control over defect properties of semiconductor materials is the key to achieving high performance and low cost devices. Therefore, the potential impact of unlocking the understanding and manipulation of defects in perovskites is great, enabling this technology to realize SETO goals. The research project is highly productive with 18 published papers in three years in top-level journals such as Science, Nature, Nature Materials, Nature Communications, Journal of American Chemistry Society, Joule, Advanced Materials, and Nano Letters. These works have drawn great attention nationwide with notable total citations over 700 times from 2020 to 2022.
The use of molecular modulators to reduce the defect density at the surface and grain boundaries of perovskite materials has been demonstrated to be an effective approach to enhance the photovoltaic performance and device stability of perovskite solar cells. Herein, we employ crown ethers to modulate perovskite films, affording passivation of undercoordinated surface defects. This interaction has been elucidated by solid-state nuclear magnetic resonance and density functional theory calculations. The crown ether hosts induce the formation of host-guest complexes on the surface of the perovskite films, which reduces the concentration of surface electronic defects and suppresses nonradiative recombination by 40%, while minimizing moisture permeation. As a result, we achieved substantially improved photovoltaic performance with power conversion efficiencies exceeding 23%, accompanied by enhanced stability under ambient and operational conditions. This work opens a new avenue to improve the performance and stability of perovskite-based optoelectronic devices through supramolecular chemistry.
Metal halide perovskite solar cells (PSCs) have risen in efficiency from just 3.81% in 2009 to over 25.2% today. While metal halide perovskites have excelled in efficiency, advances in stability are significantly more complex and have progressed more slowly. The advance of efficiency, which is readily measured, over stability, which can require literally thousands of hours to demonstrate, is to be expected given the rapid rate of innovation in the field. In the face of changing absorber composition, synthetic approaches, and device stack components it is necessary to understand basic material properties to rationalize how to enable stability in devices. In this article the aim is to present an in-depth review of the current understanding of metal halide perovskite devices and module stability by focusing on what is known retarding intrinsic and extrinsic degradation mechanisms at the material, device, and module level. Once these considerations are presented the discussion then moves to connecting different degradation mechanisms to stresses anticipated in operation and how they can impact efficiency of cells and ultimately modules over time.
Boron-doped Czochralski (Cz) Si is the most commonly used semiconductor in the fabrication of solar cells. The minority carrier lifetime of boron-doped Cz Si decreases upon exposure to light due to B–O-related defects, which reduce the performance of ~10 9 solar modules worldwide. Using electron paramagnetic resonance (EPR), we have identified the spin-active paramagnetic signatures of this phenomenon and gained insights into its microscopic mechanism. We found a distinct defect signature, which diminished when the degraded sample was annealed. The second signature, a broad magnetic field spectrum, due to the unionized B acceptors, was present in the annealed state but vanished upon light exposure. These observations show that, on degradation, nearly all the ~10 16 cm –3 B atoms in Cz Si complexed with interstitial O atoms, whereas only ~10 12 cm –3 of these complexes created defects that were recombination-active. The formation rate of these recombination-active defects correlated with the decay of the minority carrier lifetime. Furthermore, the line shape parameters linked these defects to both B and O impurities in Cz Si.
Electroluminescence, infrared imaging, and current–voltage curve techniques are used to detect and map underperforming cells in a photovoltaic module or the modules in a photovoltaic string. In this work, we present a non-contact electrostatic voltmeter technique to detect and map the underperforming spots in a cell and the cells in a module. This non-contact technique directly maps the charged surface voltage (671mV) of the superstrate glass, and it had an 11-mV difference with the voltmeter values (660mV). Another data set of voltage values obtained by electroluminescence images conversion into a voltage map showed a difference of 7mV with the non-contact voltmeter values. Further, the direct voltage values obtained at various good and poor-performing spots of the cells using this technique are 3.69V and 3.79V and are validated using the voltage values obtained in electroluminescence analysis and the difference in voltage obtained by the two techniques is determined to be less than 2%. In this work, we combine the strengths of two complementary techniques of the electrostatic voltmeter (strength: quantitative) and electroluminescence (strength: spatial mapping) to obtain a quantitative spatial mapping of defects. Furthermore, this work is extendable to detect the poor-performing modules in solar PV power plants.
Electroluminescence (EL) imaging is a powerful tool used to identify defects in photovoltaic solar cells. Typically, this type of characterization is performed in the dark using a current injection that equals short-circuit current measured at standard test conditions (STC). Restricting imaging to such a temperature range limits the information obtained about the module and cells. However, it is not trivial to develop a tool that would allow for EL imaging to be performed under a wider range of temperatures. Here we demonstrate an in situ EL imaging capability developed within an environmental chamber that allows for control of sample temperatures between –40 and 90 °C. Additionally, we demonstrate EL imaging of 4-cell mini-modules (MiMo) under front-side mechanical loading. A Raspberry Pi-connected camera with short-pass filter removed is used for EL imaging. The camera is low-cost with a small form-factor, appropriate for use in a harsh, enclosed environment. The camera is installed within a thermally isolating housing mounted within the environmental chamber. Three example cases are given for MiMos that exhibit various forms of degradation including solder fatigue and cell cracking. It is shown that by measuring at conditions above and below STC, different behaviors may be identified. In some cases, restricting characterization to STC may lead to a failure to detect damage existing in the sample.
The report describes the development of scalable manufacturing methods for high-performance, stable perovskite solar modules using flexographic printing. The project developed self-leveling perovskite inks that exploit Marangoni flows to reduce coating defects and improve large-area film uniformity. Bayesian optimization was integrated with high-throughput photoluminescence mapping and photovoltaic measurements to efficiently optimize ink formulations and printing conditions. The resulting printed perovskite solar cells achieved champion power conversion efficiencies above 21.6%, with median efficiencies exceeding 20% across large device batches. At the module scale, printed devices achieved active-area efficiencies up to approximately 17.3% on 25 cm² substrates. The project also demonstrated improved performance and stability using additively patterned interconnections compared with laser-scribed controls. Overall, the work establishes a data-driven, roll-compatible pathway toward high-throughput, low-capital-cost manufacturing of uniform and stable perovskite photovoltaics.
Photocurrent responses of amorphous thin film silicon solar modules at room temperature were studied at different excitation intensities using various monochromatic light sources. Photocurrent imaging techniques have been effectively used to locate rapidly, and non-destructively, failure and defect sites in the multilayer thin film device. Differences observed in the photocurrent response characteristics for two different cells in the same amorphous thin film silicon solar module suggest the possibility of the formation of dissimilarly active devices, even though the module is processed in the same fabrication process. Possible mechanisms are discussed.
The design of a thin-film solar cell module is dependent on the probability of occurrence of pinhole shunt defects. Using known or assumed defect density data, dichotomous population statistics can be used to calculate the number of defects expected in a module. Probability theory is then used to assign the defective cells to individual strings in a selected series-parallel circuit design. Iterative numerical calculation is used to calcuate I-V curves using cell test values or assumed defective cell values as inputs. Good and shunted cell I-V curves are added to determine the module output power and I-V curve. Different levels of shunt resistance can be selected to model different defect levels.
Defective bypass diodes are often found as the largest factor leading to power loss in solar modules. Here, we report on failure mechanisms by investigating shunted bypass diodes from a rooftop installation, using a combination of multiple characterizations including current–voltage analysis, thermal-runaway testing, X-ray computed tomography, lock-in thermography, focused ion-beam cross-section imaging, chemical decapsulation, optical microscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Differing from static discharge typically associated with lightning strikes on modules, we found diode failure by the mechanism of thermal damage under continuous, long-term overstress in forward bias. Our conclusion is based on evidence of energy dissipated—the small to medium extent of melt-through on the Schottky diode face. The diode failure shows distortions or roughening of the Schottky diode metal-semiconductor interface with the die attach, and some failures are accompanied by die-attach solder melting. We propose that nonuniform irradiance on the modules caused diode shunting due to extended periods of heat dissipation in the modules, because modules in this string were placed in two different orientations. In contrast, a second parallel module string of the same module type on the same rooftop with a unique plane of array did not show any diode failures. The thermal damage failure of melt-through was caused by the long-term current generated by overstressing of diodes that may have had crystalline and impurity defects.
Results obtained in applying the unique characteristics of the solar cell laser scanner to investigate the defects and quality of amorphous silicon cells are presented. It is concluded that solar cell laser scanners can be effectively used to nondestructively test not only active defects but also the cell quality and integrity of electrical contacts.
The commercialization of perovskite solar cells (PSCs) faces significant challenges due to their sensitivity to environmental humidity, which compromises film crystallization and device stability. Here, we introduce diphenylvinylphosphine (DPVP) as a Lewis base additive that enhances the performance and reproducibility of PSCs fabricated under ambient-air conditions. DPVP suppresses moisture-induced defect formation and stabilizes crystallization within realistic process-humidity ranges (20–40% relative humidity) commonly encountered in laboratory and pilot-scale manufacturing environments. It improves film uniformity, reduces trap densities, and yields highly reproducible device performance, enabling champion PCEs of 24.2% in small-area devices and 20.5% in blade-coated 12 cm 2 mini-modules. Furthermore, DPVP-assisted modules exhibit enhanced stability, retaining over 85% of their initial efficiency after 900 h of maximum power point tracking (MPPT) at 65 °C. This study demonstrates a humidity-resilient and scalable additive strategy for ambient-air perovskite photovoltaic manufacturing.
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 Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.
In this work, we reveal the role of non-covalent interactions, which are known to play important roles in supramolecular phenomena, in achieving efficient perovskite surface and grain boundary passivation. By using a series of pseudohalides, we find that trifluoroacetate (TFA - ) provides the strongest binding to iodide vacancies by means of non-covalent hydrogen bonding and dispersion interactions. By exploiting additional non-covalent dispersion and hydrophobic interactions in aromatic 3,3-diphenylpropylammonium (DPA + ), we present a dual-ion passivation strategy that not only minimizes the non-radiative recombination center and local chemical inhomogeneities but also induces preferentially oriented growth of α-FAPbI 3 lattice. Further, this leads to an outstanding power conversion efficiency (PCE) of 25.63% with an exceptional open-circuit voltage of 1.191 V in a perovskite solar cell with a small area, while perovskite solar mini modules with aperture areas of 25 and 64cm 2 achieved PCE of 22.47% (quasi-steady-state [QSS]-certified 20.50%) and 20.88%, respectively, with outstanding stability under high-humidity conditions.
In this research, we examine the relationship between aerial IR defect analysis and photovoltaic (PV) performance data for twelve utility- and commercial-scale solar sites in the United States. To do this, we fuse the site diagram geoJSON's, aerial infrared thermography (aIRT) defect analyses, and associated inverter time series, allowing for a direct comparison between site defects and time series data. Defect analyses were provided by Zeitview, under its Solar Insights platform. Following the data fusion process, we look at the relationship between system performance and aIRT defects. We investigate the relationship between degradation and hotspot defects, as well as the relationship between AC power data and offline strings and misaligned modules. In general, system degradation was not affected by long-term or balance-of-system (BoS) defects as they occurred infrequently in the data set. However, for one system, a near statistically significant relationship (p-value=0.057) was found when comparing the degradation of inverter blocks with several multi-hotspot defects to all other inverter blocks without this particular defect. There was strong alignment when comparing short-term recoverable module defects such as stuck trackers and offline strings to time series data. In general, we found that when an inverter block has more than 80% of modules flagged for one of these defects, its AC power time data is flat-lined and the inverter block is not producing.