Search

The temperature-dependent evolution of the photoluminescence (PL) properties of a highly emissive Cs2Ag0.35Na0.65Bi0.02In0.98Cl6 perovskite was examined in the range of 80–340 K. The PL maximum exhibited a shift to higher energies with an increase in T, while the bandgap decreased with elevated T. This behavior can be explained by a thermal distribution of the self-trapped exciton states.

Multijunction solar cells offer a path to very high conversion efficiency, exceeding 60% in theory. Under ideal conditions, efficiency increases monotonically with the number of junctions. In this study, we explore technical and economic mechanisms acting on tandem solar cells. We find that these mechanisms produce limitations that are the more pronounced the greater the number of junction is and, hence, limit the ideal number of junctions, as well as the corresponding efficiencies. Spectral variations induce current losses in series-connected tandem solar cells. For Denver, we find that these losses reduce achievable harvesting efficiencies to 51% for non-concentrated light, and that they restrict the ideal number of junctions to less than nine. Independently operated solar cells suffer from optical losses with similar consequences. Optical efficiencies of 99% restrict the ideal number of junctions to below ten, and reduce achievable efficiencies by more than 10%. Only architectures with a sequential cell illumination are more resilient to these losses. Restricting available materials reveals that a sufficiently low band gap for the bottom cell of 0.9 eV or below is expedient to realize high efficiencies. Economic considerations show that five junctions or less are economically ideal for most conceivable applications.

To identify abnormal photovoltaic (PV) modules in large‐scale PV plants economically, drone‐mounted infrared (IR) cameras and automated video processing algorithms are frequently used. While most related works focus on the detection of abnormal modules, little has been done to automatically localize those modules within the plant. In this work, we use incremental structure‐from‐motion to automatically obtain geocoordinates of all PV modules in a plant based on visual cues and the measured GPS trajectory of the drone. In addition, we extract multiple IR images of each PV module. Using our method, we successfully map 99.3% of the 35,084 modules in four large‐scale and one rooftop plant and extract over 2.2 million module images. As compared to our previous work, extraction misses 18 times less modules (one in 140 modules as compared to one in eight). Furthermore, two or three plant rows can be processed simultaneously, increasing module throughput and reducing flight duration by a factor of 2.1 and 3.7, respectively. Comparison with an accurate orthophoto of one of the large‐scale plants yields a root mean square error of the estimated module geocoordinates of 5.87 m and a relative error within each plant row of 0.22 m to 0.82 m. Finally, we use the module geocoordinates and extracted IR images to visualize distributions of module temperatures and anomaly predictions of a deep learning classifier on a map. While the temperature distribution helps to identify disconnected strings, we also find that its detection accuracy for module anomalies reaches, or even exceeds, that of a deep learning classifier for seven out of ten common anomaly types. The software is published at https://github.com/LukasBommes/PV-Hawk.

A new single-step and green approach for the synthesis of microcrystalline Cs2AgxNa1−xBiyIn1−yCl6 (CANBIC) perovskites in ambient conditions is introduced. The CANBIC powders emit broadband self-trapped excitonic photoluminescence (PL) with a champion PL quantum yield (QY) of 98 ± 2% and a PL lifetime of ca. 2 μs observed for x = 0.40 and y = 0.01–0.02. The study focuses on the dependence of structural, spectral, and photophysical properties of CANBICs on Bi content. CANBICs are solid solutions with isomorphous In-to-Bi substitution with the bandgap and valence band edge energy decreasing gradually with increasing y. The PL QY and the rate constant of the radiative recombination showed volcano-shaped dependences on the Bi content, while the rate of the non-radiative recombination revealed a drastic growth by three orders of magnitude as the Bi fraction y was elevated from 0.01 to 1.0 indicating that BiCl6 units are responsible for non-radiative recombination.

Maßgeschneiderte Modifizierungen bleifreier Halogenid-Perowskite (BHP) durch Dotierung/Legierung mit Metallkationen gelten als vielversprechender Weg zu hocheffizienten anorganischen Leuchtstoffen mit Photolumineszenz (PL)-Quantenausbeuten von bis zu 100 %. Solche Materialien weisen typischerweise eine selektive Empfindlichkeit gegen UV-Licht, einen breiten PL-Bereich, lange PL-Lebensdauern und eine einzigartige Variabilität und Stabilität der Zusammensetzung auf – eine ideale Kombination für viele Lichtsammelanwendungen. Dieser Kurzaufsatz stellt den Stand der Technik bei dotierten BHPs vor, mit dem Schwerpunkt auf Berichten aus den letzten 2–3 Jahren. Wir diskutieren die Faktoren, die die Effizienz und die spektralen Parameter der Breitband-PL von dotierten BHP abhängig von Dotierstoff und Wirtsmatrix im mikro- wie auch im nanokristallinen Zustand bestimmen, gehen auf die wichtigsten Herausforderungen in diesem sich rasch entwickelnden Forschungsbereich ein und skizzieren die vielversprechendsten Konzepte, um weitere Fortschritte in diesem Bereich zu erzielen.

Following the 2nd release of the “Emerging PV reports,” the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer‐reviewed articles in academic journals since August 2021. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open‐circuit voltage, short‐circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, e.g., the detailed balance efficiency limit. The 3rd installment of the “Emerging PV reports” extends the scope toward triple junction solar cells.

Doping with In(iii) induces the formation of stable and phase-pure Cs2AgxNa1−xFeyIn1−yCl6 perovskites with bandgaps close to 2 eV.

Thermal deposition of halide perovskites as a universal and scalable route to transparent thin films becomes highly challenging in the case of lead-free double perovskites, requiring the evaporation dynamics of multiple metal halide sources to be balanced or a single-phase precursor preliminary synthesized to achieve a reliable control over the composition and the phase of the final films. In the present Letter, the feasibility of the single-source vacuum deposition of microcrystalline Cs2AgxNa1-xBiyIn1-yCl6 double perovskites into corresponding transparent nanocrystalline films while preserving the bulk spectral and structural properties is shown. The perovskite films produced from the most emissive powders with x = 0.40 and y = 0.01 revealed a photoluminescence quantum yield of 85%, highlighting thermal evaporation as a promising approach to functional perovskite-based optical materials.

Tailored modifications of halide lead‐free perovskites (LFPs) via doping/alloying with metal cations have been recognized as a promising pathway to highly efficient inorganic phosphors with photoluminescence (PL) quantum yields of up to 100 %. Such materials typically display selective sensitivity to UV light, a broad PL range, and long PL lifetimes as well as a unique compositional variability and stability—an ideal combination for many light‐harvesting applications. This Minireview presents the state‐of‐the‐art in doped LFPs, focusing on the reports published mostly in the last two to three years. We discuss the factors determining the efficiency and spectral parameters of the broadband PL of doped LFPs depending on the dopant and host matrix, both in micro‐ and nanocrystalline states, address the most relevant challenges this rapidly developing research area is facing, and outline the most promising concepts for further progress in this field.

Feasibility of the identification of backsheet (BS) composition of field-aged Si photovoltaic (PV) modules based on the analysis of characteristic patterns of UV-excited fluorescence (UVF) of ethylene vinyl acetate copolymer encapsulant is shown. The approach includes BS identification by vibrational spectroscopies and the formation of a database of BS-related characteristic UVF patterns. A multivariate principal component analysis of the UVF images of unknown samples combined with the UVF pattern database allows the BS type to be identified only from UVF images. The geometric place of particular samples in the principal component plots is indicative of the general degradation status of PV modules. The proposed method can be applied for high-throughput noncontact characterization of solar PV plants.

It is commonly assumed that photovoltaic (PV) modules have a 25-year lifetime. Warranties last this long, and techno-economic projections use this figure when calculating system performance. Yet, PV modules do not stop functioning after this time. Many modules still perform at above 80% of their original power rating after 25 years, and there is neither an economic nor an ecological reason to retire them. In this study, we explore the implications of transitioning from a mindset with finite module-life to a long-term perspective for PV-modules. We introduce an economic model to reflect such a long-term view. Using this model, we show that degradation gains in importance compared to module price. We calculate a cost-entitlement of up to 12ct/W for utility installation when improving annual degradation rates from 0.5% to 0.2%. We show that maintenance becomes more valuable – about 33% compared to a system with 30-year lifetime - and that time-constraints for maintenance disappear. We define a new lifetime metric - the minimum economically useful lifetime of a PV module as the time the module needs to be operated before replacement becomes economically preferable. We find that modules currently installed should be operated for at least 25 years and ideally for 50 years. Our metric links module life with degradation rate and, we calculate that reducing annual degradation rates from 0.5% to 0.2% increases the useful lifetime by a factor of 1.69. Such an increase would defer module end of life by decades and reduce resources required for maintenance by 40%.

Degradation of backsheets (BSs) of commercial silicon PV modules is currently recognized as a source of reduced module performance and module failure. Monitoring of the BS state in the field is possible by using non-destructive and highly informative near-infrared absorption (NIRA) spectroscopy. Application of NIRA for the analysis of multi-layer polyethylene terephtalate (PET) based BSs, which dominate the PV module market, is challenging due to a large variety of possible BS configurations that show only small differences in NIRA spectra. In the present work, a spectroscopic tool for the structural identification of PET-based BSs is introduced. The method is based on a principal component analysis of a database of 250 representative NIRA spectra of BSs of different types. It allows a BS with an unknown structure to be assigned to one of 12 different types based solely on its NIRA spectrum. The identification was successfully validated on a test collection of 45 selected BSs and shown to be feasible for the field deployment. Further automation of NIRA measurements and spectral analysis are expected to elevate the proposed tool to the level of a non-intrusive high-throughput field analysis of the BS composition and state in operating PV module grids.

In this study, we introduce a weather data independent self-referencing algorithm for performance analyses of PV power stations based on monitoring data. We introduce this method as an alternative to standard performance ratio analyses, in the case that no meteorological data are available. The self-referencing algorithm utilizes the string with maximum output as a reference. The output of other strings is set in relation to the corresponding value of the reference string, resulting in a quantity we call self-referencing ratio SR. We benchmark this ratio against the established performance ratio, which requires solar irradiance data for calculation. Based on a 9 MWp PV power station with 1,719 strings, we show that we are able to quantify, locate, and visualize the performance of each string in the PV power station. About 10% of the strings are underperforming; most of them are located in the bottom rows of the mounting racks. Shading from adjacent mounting racks is a possible explanation for this finding. Furthermore, IR-images and voltage data indicate a frequent occurrence of strings with a reduced number of modules. An underperformance of less than 2% is challenging to detect, due to the uncertainty range of the chosen dataset.

Summary Evaluating the potential of organic photovoltaic (OPV) materials and devices for industrial production is a multidimensional optimization process with an incredibly large parameter space. Here, we demonstrate automated OPV material and device characterization in terms of efficiency and photostability. Gaussian process regression (GPR) prediction based on optical absorption features guided the optimization process with promising prediction accuracy for PV parameters and burn-in losses. With ∼100 process conditions, screening for efficiency and photostability can be finished within 70 h. The highest power conversion efficiency (PCE) of 14% was achieved by fully automated device fabrication in air with a model material system PM6:Y6. Improving molecular ordering has been identified as the most promising motif for further efficiency optimization. Thin active layers combined with medium thermal annealing temperature are favorable to simultaneously improve efficiency and suppress burn-in losses. The platform and protocol may be expanded to any solution-processed organic semiconductor and interface materials.

Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning has become popular for this. However, related works typically sample train and test data from the same distribution ignoring the presence of domain shift between data of different PV plants. Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labeled data of one source PV plant and make predictions on another target plant. We train a ResNet‐34 convolutional neural network with a supervised contrastive loss, on top of which we employ a k‐nearest neighbor classifier to detect anomalies. Our method achieves a satisfactory area under the receiver operating characteristic (AUROC) of 73.3% to 96.6% on nine combinations of four source and target datasets with 2.92 million IR images of which 8.5% are anomalous. It even outperforms a binary cross‐entropy classifier in some cases. With a fixed decision threshold, this results in 79.4% and 77.1% correctly classified normal and anomalous images, respectively. Most misclassified anomalies are of low severity, such as hot diodes and small hot spots. Our method is insensitive to hyperparameter settings, converges quickly, and reliably detects unknown types of anomalies making it well suited for practice. Possible uses are in automatic PV plant inspection systems or to streamline manual labeling of IR datasets by filtering out normal images. Furthermore, our work serves the community with a more realistic view on PV module fault detection using unsupervised domain adaptation to develop more performant methods with favorable generalization capabilities.

Solution-based processing of photovoltaic materials has important advantages, including lower overall costs and easier upscaling of fabrication, where the solvents play an essential role. Antisolvent crystallization technique is often applied to achieve high-quality perovskite materials. However, the basic chemistry of the solvent-antisolvent crystallization is still not well understood. Besides, common antisolvents are toxic, and their number is limited. In this work, a novel robot-based method was used to screen the efficient antisolvents for different solvent-perovskite systems. 336 combinations of perovskite-solvent-antisolvent could be prepared and characterized by the integrated robotic platform in 2 days. In addition, we provided a detailed working mechanism of the solvent-antisolvent crystallization approach. Furthermore, hundreds of potential antisolvents were proposed based on high-throughput screening and simulation. Verification tests match well with theory, and all reported antisolvents used for photovoltaic device optimizations are within our predicted range of the Hansen space, indicating that the predictions are reliable. In terms of solution-processed lead halide perovskite optoelectrical materials, suitable antisolvent is essential for the crystallization and properties of final products. Conventional experiments of antisolvent selections are based on the manual time-consuming trial-error methods, which cannot meet today's increasing demands. In this work, a robot-based method was used to screen the antisolvents for different solvent-perovskite systems in a high-throughput manner to study the influence of interactions among the solvent molecules, cations, metal-halides, and antisolvents, to build an informative database, and to reveal the working mechanism and selection criteria of antisolvents. Besides, simulations based on the screening results provide hundreds of potential antisolvents. Corresponding verification tests evidenced that our predictions are reliable and vital for designing future efficient mixed solvent systems for the fabrication of perovskite optoelectrical materials. A robotic platform is adopted to conduct a comprehensive solvent engineering for making lead halide perovskites in a high-throughput manner. Deeper insights into the working mechanisms and selection criteria of antisolvents are investigated and summarized. In addition, a reliable antisolvent database is established, and verification tests match well with the theory. Furthermore, our work provides significant guidance for designing functional and environment-friendly mixed solvent systems to fabricate high-quality perovskite materials or devices.

A lot of effort is required in order to close the efficiency gap between laboratory record Cu(In,Ga)Se 2 (CIGS) solar cells and commercially produced modules. An obstacle easily overlooked is the fact that reduced module performance may have multiple root causes. Here, we show how to isolate two main factors that influence the open circuit voltage ( V oc ) of photovoltaic devices, the material composition and the local defects, here shunts in particular, i.e., regions of significantly lowered resistivity. In order to characterize the material composition, we used electroluminescence spectroscopy. Additionally, we investigated the effect of local defects on the V oc , by the use of thermography. Which of both factors dominates the resulting V oc depends on the insolation intensity under which the device is operated. We established a model in order to estimate the V oc as a function of the luminescence peak wavelength, the quantification of the thermal hot spots caused by local defects, and the insolation intensity. Subsequently, we investigated how these factors affect the electrical conversion efficiency of the device. The combination of these two measurement techniques results in a more conclusive evaluation of the electrical parameters, thereby enabling an improved quality assessment to allow a successive process optimization. This approach is also applicable to other thin film PV technologies such as perovskite and organic solar cells.

Reliable and durable solar power plants require PV modules with high-grade polymer encapsulants and backsheets (BSs). For performance analyses of PV installations fast, reliable and non-destructive methods for determining composition and degradation state of polymer components need to be developed. Here, we show that the structure of some common polymer BSs can be determined in the field in real time by analyzing near-infrared transmission (NIRT) spectra collected under illumination with natural sunlight. The potential of this “Solar-NIRT” method was probed by field measurements on a multi-MW PV power plant where four major BS types were identified by multispectral cross-sectional Raman imaging. Additionally, degradation of a particular BS type was found to result in distinct changes in NIRT spectra allowing the degraded BSs to be classified as a separate type. Principal component analysis (PCA) applied to a collection of 62 Solar-NIRT spectra allowed to create a map of five clusters, each corresponding to a particular BS type. The feasibility of using the PCA cluster map for the identification of unknown samples was shown on a test set of 13 different BSs. The Solar-NIRT is relatively fast, non-invasive, selective, can be upgraded to a non-contact regime making it a promising tool for high-throughput characterization.

A new “green” and mild synthesis of highly stable microcrystalline Cs2AgxNa1−xBiyIn1−yCl6 (CANBIC) perovskites under ambient conditions was developed that is scalable to the multi-gram production. Under UV illumination, the CANBIC perovskites emit intense broadband photoluminescence (PL) with a quantum yield (QY) of 92% observed for x = 0.35 and y = 0.01–0.02. The combination of strong UV absorbance and broadband visible emission, high PL QY, and long PL lifetimes of up to 1.4 μs, along with an outstanding stability makes these CANBICs a promising material class for many optical applications.Graphical abstract: “Green” synthesis of highly luminescent lead-free Cs2AgxNa1−xBiyIn1−yCl6 perovskitesLead halide perovskites have gone through an unprecedented rapid development from one of many promising photovoltaic materials to a real competitor for established photovoltaic absorbers such as silicon, copper–indium–gallium chalcogenides, and CdTe.1–4 This triggered the search for novel lead-based perovskite compounds and particularly for “greener” lead-free structural analogs with comparable optical and photovoltaic characteristics. Due to the many possible compositions of lead-free perovskites (LFPs), especially of the so-called double LFPs with a couple of Pb2+ being substituted by M+M3+ combinations,1,3 there are hundreds of structures which could be identified as promising candidates for photovoltaics, light emission, and light management (down-shifting, up-conversion, etc.).1,3–6 Given the broad variability of alkali metal and halide positions in LFPs, these structures translate into thousands of compounds to be synthesized and tested for light-conversion applications which presents a huge experimental challenge.LFP tunability can be exemplified by In-based double compounds, A2MIInX6 (A – alkali metals, X – halides), where all four positions can be independently varied. In particular, MI can be occupied by Na+, K+, Ag+ or their mixtures, A – by Cs+, Rb+, or organic cations, and X – by individual and mixed halides. At the same time, InIII can be substituted by a number of MIII cations, including BiIII and SbIII.1,4 The interest in such compounds, particularly in Bi-doped Cs2AgxNa1−xInCl6 (referred to as CANIC) or Cs2AgxNa1−xBiyIn1−yCl6 (referred to as CANBIC), is constantly growing due to their intense broadband photoluminescence (PL) combined with a unique tunability of the photophysical properties and an outstanding chemical stability.5–21In view of this compositional variability, the progress in the material science of LFP requires a transition from conventional single-compound trials to high-throughput studies based on robot-assisted synthesis and characterization, ideally supported by machine-learning-based data analysis.22–24 The feasibility and efficiency of high-throughput experiments in the field of lead perovskite photovoltaics were recently demonstrated.25–30However, the majority of the reported syntheses of double LFPs require high energy input by hydrothermal treatment or calcination. Furthermore, a considerable amount of manual intermediate steps is needed which cannot be automated, as well as the usage of hazardous chemicals such as concentrated HCl in the case of chloride LFPs1,3,6,7,9,14,17,21 or unstable and aggressive substances for bromide and iodide LFPs.1,3,21 More favorable milder temperature-, saturation-, or antisolvent-driven precipitation approaches, conventionally employed for the fabrication of lead-based perovskites, often yield poorly controlled mixtures of perovskite and non-perovskite phases when applied to double LFPs.1,13,31 As a result, the development of simple and mild procedures for the reproducible and phase-controlled synthesis of double LFPs under ambient conditions with robotized equipment still remains challenging.In this communication, we introduce such an approach to the fabrication of microcrystalline lead-free CANIC and CANBIC perovskites, yielding compositionally controlled single-phase products under ambient conditions in a single step without any additional thermally activated steps or the need for hazardous chemicals. By focusing on the optical properties of the most strongly emissive CANBIC perovskite composition, we show that our approach results in a very stable and structurally perfect material emitting broadband PL in the visible spectral range with a PL quantum yield (QY) close to unity.

Over the last years, a significant number of inverter shutdowns most likely due to degraded backsheets have been reported by operators, investors, and manufacturers. We investigated water ingress into different backsheets, and the resulting risk for inverter shutdowns. For studying pending insulation issues of inverters, we analyzed exemplarily a 5-MWp photovoltaic (PV) power station with 20,530 PV modules and 314 inverters. For backsheet identification on-site near-infrared absorption spectroscopic measurements of 518 PV modules from 20 inverters were carried out. In the lab, wet leakage tests provide kinetic data of insulation resistances for different backsheet materials. Historic ground impedance data logged by the inverter were evaluated to show the temporal evolution of inverters with different backsheets. We observe different kinetics and drops in leakage resistance for different backsheet types based on polyamide (PA) and air-side fluorinated coating (FC), and distinguish between two types of behavior. “PA-like” is characterized by moderate leakage resistance loss and slower performance reduction with regard to insulation in the field. “FC-like” is characterized by high susceptibility to water and a high leakage resistance loss rate for PV modules after 6–8 years of operation. These characteristics are indicated when measuring inverter GI in dependence of temperature and humidity. For PA-based BSs, the reduction in ground impedance (GI) is steady over 8 years. We observe incidences with GI below the critical value of 400 kΩ on 2.8% of all days. For FC-based BSs, we also observe steady decrease of average GI initially, yet after 6 years in the field, we see a sudden and rapid decline for 20% of those inverters. GI falling below the critical value is observed on up to 5% of all days, with the trend increasing. Therefore we, expect this problem to become even more severe for longer operating times.

We present here a degradation study of a PV power plant consisting of several module pairs connecting each to a micro-inverter with monitoring. The modules comprise cell cracks, cell breakages and edge shunting. The impact of using different methodologies on the resulting relative yield loss rates is analyzed. It is demonstrated how these methods can be altered to circumvent missing irradiation sensor data generating a substitute for a relative degradation rate. The relative degradation rate of cell cracks is close to the reference with all methods and near to the detection limit which indicates little degradation for cell cracks beyond the material-inherent degradation. The modules with cell breakages and edge shunting show a higher relative yield loss of l-3 %/a depending on the method of analysis. This indicates quite a variation is possible depending on the method thus warranting caution when evaluating degradation rates or their substitutes. It was shown that cell breakages perform increasingly worse in summer leading to even higher but seasonal performance losses. Because of that, the relative yield loss can be much higher than just the relative loss in rated power due to probably averse temperature effects on defect performance. To better assess cell crack degradation, either longer observation periods or more precise methods are needed

The long-term stability of perovskite solar cells remains a challenge. Both the perovskite layer and the device architecture need to endure long-term operation. Here we first use a self-constructed high-throughput screening platform to find perovskite compositions stable under heat and light. Then, we use the most stable perovskite composition to investigate the stability of contact layers in solar cells. We report on the thermal degradation mechanism of transition metal oxide contact (for example, Ta-WOx/NiOx) and propose a bilayer structure consisting of acid-doped polymer stacked on dopant-free polymer as an alternative. The dopant-free polymer provides an acid barrier between the perovskite and the acid-doped polymer. The bilayer structure exhibits stable ohmic contact at elevated temperatures and buffers iodine vapours. The unencapsulated device based on the bilayer contact (with a MgF2 capping layer) retains 99% of its peak efficiency after 1,450 h of continuous operation at 65 °C in a N2 atmosphere under metal-halide lamps. The device also shows negligible hysteresis during the entire ageing period.

Polymers are essential for long-term green energy production by photovoltaics (PV). We created a unique dataset that links electrical performance data of 28,030 PV modules, combined into 1295 strings and 423 inverters with the identity of the backsheet (BS) materials of each of the tested modules. This dataset gives an insight into BS-versatility and resulting impact on the insulation resistance of strings and inverters. The BSs were analyzed using near infrared absorption spectroscopy (NIRA) plus visual inspection in the field. The insulation resistance (Riso) of selected modules and strings was measured on-site. Furthermore, reported ground impedance (GI) values were evaluated with respect to an inverter-specific threshold below which the inverters do not start operation. We differentiated seven BS-types, the three most frequent being polyamide (PA), fluorinated coating (FC), and polyvinylidene fluoride (PVDF). Although modules with PA show often BS cracking, the insulation resistance was above the threshold. Similar, the GI of inverters with modules with PVDF-BSs was high. Modules with FC-BSs feature generally low Riso–values on module-, string-, and on inverter level. Evaluating historic data reveals an increase in frequency of GI-values below the threshold for inverters connected to modules with exclusively FC-BSs. While inverters with modules having PA- or PVDF-BSs function as expected, inverters with modules having FC-BSs require maintenance to keep power yield on an acceptable level. The dataset collected in the present work highlights that the choice of BS-material of PV-modules is essential for reliable operation of PV power stations.

Cost-effective, fast, and non-destructive, on-site photovoltaic (PV) characterization methods are of interest to PV operators to determine countermeasures against defects causing power loss or against safety problems. Combining the advantages of both methods electroluminescence (EL) and thermography is photoluminescence (PL). With our PL setup, we achieved high resolution luminescence images of large area PV modules without any physical and electrical contact. Defects are recognizable with a high rate compared to indoor EL images at controlled conditions. We analyzed inactive areas, cracks, potential induced degradation, snail trails, EVA degradation, and interconnection failures and compared the PL images with different characterization methods.

In this work we introduce a combination of spectral, imaging and electric lab and field tests that complement field I-V and power measurements and allow to track the insulation failures of the solar modules and associate various failure modes with different types of the module backsheets.

Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi‐automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107,842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet‐50 to classify ten common module anomalies with more than 90% test accuracy. Experiments show that our tool generalizes well to different PV plants. It successfully extracts PV modules from 512 out of 561 plant rows. Failures are mostly due to an inappropriate UAV trajectory and erroneous module segmentation. Including all manual steps our tool enables inspection of 3.5 MWp to 9 MWp of PV installations per day, potentially scaling to multi‐gigawatt plants due to its parallel nature. While we present an effective method for automated PV plant inspection, we are also confident that our approach helps to meet the growing demand for large thermographic datasets for machine learning tasks, such as power prediction or unsupervised defect identification.

Emerging photovoltaics (PVs), focuses on a variety of applications complementing large scale electricity generation. For instance, organic, dye-sensitized and some perovskite solar cells are considered in building integration, greenhouses, wearable and indoors, thereby motivating research on flexible, transparent, semitransparent, and multi-junction PVs. Nevertheless, it can be very time consuming to find or develop an up-to-date overview over the state-of-the-art performance for these systems and applications. Two important resources for record research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley-Queisser limit. In all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield (STEY) is included as an analysis parameter among state-of-the-art emerging PVs.

Emerging photovoltaics (PVs) focus on a variety of applications complementing large scale electricity generation. Organic, dye-sensitized, and some perovskite solar cells are considered in building integration, greenhouses, wearable, and indoor applications, thereby motivating research on flexible, transparent, semitransparent, and multi-junction PVs. Nevertheless, it can be very time consuming to find or develop an up-to-date overview of the state-of-the-art performance for these systems and applications. Two important resources for recording research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley-Queisser limit. in all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield is included as an analysis parameter among state-of-the-art emerging PVs., VDI/VD Innovation + Technik GmbH; SAOT - German Research Foundation (DFG)German Research Foundation (DFG); DFGGerman Research Foundation (DFG)European Commission [INST 90/917-1 FUGG, 182849149, SFB 953]; Energy Conversion Systems-from Materials to Devices [IGK 2495]; grant "ELF-PV-Design and development of solution processed functional materials for the next generations of PV technologies" [44-6521a/20/4]; grant "Solar Factory of the Future" [FKZ 20.2-3410.5-4-5]; SolTech Initiative by the Bavarian State Government; FAPESPFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2017/11986-5]; ShellRoyal Dutch Shell; ANP (Brazil's National Oil, Natural Gas and Biofuels Agency) through the R&D levy regulation; National Science FoundationNational Science Foundation (NSF) [CBET-1702591]; US Department of Energy, Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies OfficeUnited States Department of Energy (DOE) [34351]; European Research Council under the European Union's Horizon 2020 research and innovation program [742708]; Projekt DEAL, O.A. acknowledges the financial support from the VDI/VD Innovation + Technik GmbH (Project-title: PV-ZUM) and the SAOT funded by the German Research Foundation (DFG) in the framework of the German excellence initiative. C.J.B. acknowledges funding from DFG within INST 90/917-1 FUGG, the SFB 953 (DFG, project no. 182849149) and the IGK 2495 (Energy Conversion Systems-from Materials to Devices). C.J.B. further acknowledges the grants "ELF-PV-Design and development of solution processed functional materials for the next generations of PV technologies" (No. 44-6521a/20/4) and "Solar Factory of the Future" (FKZ 20.2-3410.5-4-5) and the SolTech Initiative by the Bavarian State Government. A.F.N. acknowledges support from FAPESP (Grant 2017/11986-5), Shell and the strategic importance of the support given by ANP (Brazil's National Oil, Natural Gas and Biofuels Agency) through the R&D levy regulation. R.R.L. gratefully acknowledges support from the National Science Foundation under grant CBET-1702591. N.K. acknowledges funding by the US Department of Energy, Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies Office, Agreement Number 34351. J.N. thanks the European Research Council for support under the European Union's Horizon 2020 research and innovation program (grant agreement No 742708).; Open access funding enabled and organized by Projekt DEAL.

Quasi-2D metal-halide perovskites with Ruddlesden−Popper structures have shown promising stability due to the protectiveeffects of the intercalating organic cations. However, a systematic studyof the effect of intercalating organic cations on stability has rarely beenreported. Here we use a high-throughput-robot platform to fabricateover 300 perovskite films and study the effect of cations and theirconcentrations on the thermal stability of perovskite films. We find thatapproximately 20−25 mol % of intercalating organic cations intoMAPbI3 (nominal n = 4/5) can maximize the film stability, whilehigher/lower concentrations lead to inferior stability, which is termedstability bowing in analogy to band-gap bowing. A model with twocompetitive effects of the intercalating organic cation (better protectionvs more defects) is proposed to rationalize this behavior. We anticipatethis work to provide new insights into the stability of quasi-2Dperovskites.

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (, Current view of the impact of A-site cation on the stability of perovskite materials and devices is derived from accelerated ageing tests at high temperature, which is beyond normal operation range. Here, the authors reveal the great impact of ageing condition on assessing the photothermal stability of mixed-cation perovskites using high-throughput robot system coupled with machine learning.

This study links chemical analysis of PV-module’s backsheet components with the power output of PV-systems during their lifetime. Several PV-systems all with two or more module types and also backsheet types were investigated. We identified three classes of backsheet types with respect to their influence on the module performance. While PV-modules with robust backsheets (e.g. double-fluoropolymers) perform constantly well over years, others show linear or even exponential performance losses, while others display total failure (e.g. single-fluoropolymers or non-fluoropolymers). Humidity is a most probable driving factor for the faster ageing process of modules with critical backsheets in terms of their chemical layer components and the resulting properties.

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self-driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compositions with less than 1 mg.

This paper presents a novel approach for the automated identification of electrically impaired strings in photovoltaic (PV) systems, which is solely based on monitored inverter data. By shifting the inverter operating points of two or more strings to a common voltage level on the direct current side, the effect of defects such as bypassed substrings is actively amplified so that defects can be detected reliably. The presented Common Voltage Test is illustrated using an electrical simulation based on the extended two-diode model. The effectiveness of the proposed approach is demonstrated using real monitoring data of an inverter in a PV power plant in Bavaria, Germany. For validation, electrically defective PV modules within the PV system are identified and located using aerial infrared thermography. Facilitating an automated reliable detection of faulty strings in PV systems, the approach is especially useful for large-scale PV power plants since only the faulty strings need to be examined in greater detail. Consequently, the maintenance becomes more efficient so that the maintenance costs may be reduced significantly.

Degradation of backsheets (BS) and encapsulant polymer components of silicon PV modules is recognized as one of the main reasons for losses in PV plant performance and lifetime expectations. Here, we report first insights into the correlation between BS composition of PV-modules and PV power station performance by using a combination of lab- and field-imaging, as well as spectroscopic and electrical characterizations. Using field-suitable near-infrared absorption (NIRA) spectroscopy, the BS structure of 518 PV-modules, 2.5 percent of the PV-modules in a 5 MWp PV power station, was identified on-site. The variance of the BS composition was found to be heterogeneous across PV-modules of the same power class from the same manufacturer. Polymaide-based BS cause in 10 out of 100 inverters ground impedance values below 400 kOhm, which is a typical threshold for inverters connecting to the grid. For primer-based BS this low value is reached 20 times. We conclude these numbers best as possible from the available monitoring data of inverters associated with BS-types from NIRA. Challenging is the identification of degraded primer-based BSs, since visually they look healthy and undistinguishable to well performing BSs. The present results demonstrate that a deeper understanding of the relationship between bill-of-materials and performance of PV-modules is necessary to avoid/minimize inverter shut-downs, and that it can be achieved by using combinations of selected field and lab characterization methods with monitoring data., 23 pages, 9 figures

The feasibility of a high-throughput robot-assisted synthesis of complex Cu1-xAgxInSySe1-x (CAISSe) quantum dots (QDs) by spontaneous alloying of aqueous glutathione-capped Ag–In–S, Cu–In–S, Ag–In–Se, and Cu–In–Se QDs is demonstrated. Both colloidal and thin-film core CAISSe and core/shell CAISSe/ZnS QDs are produced and studied by high-throughput semiautomated photoluminescence (PL) spectroscopy. The silver-copper-mixed QDs reveal clear evidence of a band bowing effect in the PL spectra and higher average PL lifetimes compared to the counterparts containing silver or copper only. The photophysical analysis of CAISSe and CAISSe/ZnS QDs indicates a composition-dependent character of the nonradiative recombination in QDs. The rate of this process is found to be lower for mixed copper-silver-based QDs compared to Cu- or Ag-only QDs. The combination of the band bowing effect and the suppressed nonradiative recombination of CAISSe QDs is beneficial for their applications in photovoltaics and photochemistry. The synergy of high-throughput robotic synthesis and a high-throughput characterization in this study is expected to grow into a self-learning synthetic platform for the production of metal chalcogenide QDs for light-harvesting, light-sensing, and light-emitting applications.

Wide bandgap mixed halide perovskites ABX3, in which X can be I, Br, or Cl, are promising materials to form highly efficient optoelectronic devices, because the optical bandgap can be controlled over a wide range by variation of the halogen composition. However, significant nonradiative losses must be overcome to approach the efficiency limit of single-junction solar cells. Here, we present a high throughput-based investigation of the influence of processing parameters on nonradiative losses in the perovskite bulk. We perform antisolvent crystallization during spin coating and vary the solvent type, its volume, and the temperature of the subsequent annealing step. We use the photoluminescence quantum yield (PLQY) as a proxy to the presence of nonradiative losses and PL spectra as a qualitative probe for sample morphology. Using Gaussian process regression, we find that we can reliably predict PLQY from the PL spectral shape. This means that the PL spectral shape conveys the essential photophysics controlling PL quenching and thus nonradiative charge recombination. In comparison with scanning electron micrographs and x-ray diffraction data, we find that nonradiative losses in polycrystalline perovskite films are caused by increased domain size dispersion. Our method provides a simple and fast structure-sensitive in-line probe for fast morphology optimization in a high-throughput fashion.

Two testing methods for photovoltaic (PV) plants, an infrared (IR)-measurement aerial system and a monitoring system on module level, are compared with respect to their capabilities for identifying irregularities of PV panels in a PV plant. For the first method, a hypothesis is tested that infrared temperature and module power can be correlated with an empirical linear correlation using the raw measurement data of the second method. A workaround is proposed how to quantify power losses for unknown PV plants showing at the same time how power losses depend on ambient conditions. Also, the data is used to improve results from the IR-measurements. The second method is explained to compare the two methods. It is shown that each method offers different advantages, helping to properly assess and schedule O&M measures.