In this study, we present a power-centric enhancement to current collection efficiency (CCE) mapping, introducing the concept of power collection efficiency (PCE). CCE, defined as the derivative of the cell current with respect to locally generated current, quantifies current transport efficiency (ranging from 0 to 1) to the device terminals. Traditionally, current efficiency maps are generated via electroluminescence imaging. Here, we propose generating PCE maps that quantify the ratio of locally generated power within the solar cell to the extracted power at the contacts. This approach captures both current and voltage losses, providing a more comprehensive assessment of power dissipation mechanisms. We demonstrate the proposed method on a silicon heterojunction solar cell under constant illumination, highlighting its potential for detailed, spatially resolved power loss diagnostics.

Long-term outdoor testing of photovoltaic (PV) modules is critical for assessing reliability but is often challenged by inconsistent and noisy data, necessitating robust filtering techniques. Traditional threshold-based filtering methods (e.g., irradiance >300W/m2) are inadequate for capturing complex data patterns inherent to outdoor data. This study presents an advanced multi-dimensional filtering framework combining Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for unsupervised classification and visualization. Our approach effectively identifies meaningful data classes, such as conditions of partial shading and fluctuating irradiance levels, enabling the development of targeted filters. Additionally, we employ the Local Outlier Factor (LOF) to detect and remove anomalies. To demonstrate the effectiveness of class-specific filtering—such as the exclusion of partially shaded data—combined with LOF-based anomaly detection, we correct outdoor measurements to Standard Test Conditions (STC) using IEC 60891:2021. Our results show that this methodology achieves higher accuracy than conventional threshold-based filtering while preserving a larger proportion of the data.

Outdoor data are essential to study the reliability of PV modules and systems. Each electrical performance measure is dependent on the conditions the measurement is conducted at and, therefore, needs to be considered in the context of dynamically changing outdoor conditions. In this paper, we introduce a statistical model designed to analyze PV outdoor data. This model uses a timeseries of current-voltage (IV) characteristics, alongside meteorological data, including plane-of-array irradiance (GPOA$$ {G}_{\mathrm{POA}} $$) and module temperature (TMod$$ {T}_{\mathrm{Mod}} $$). The model aims to utilize all available information to predict the respective performance measure as well as its uncertainty at arbitrary conditions and times. First, to ensure its quality and relevance, a suitable filtering approach is applied to the IV curves, GPOA$$ {G}_{\mathrm{POA}} $$ and TMod$$ {T}_{\mathrm{Mod}} $$ data from nine modules from five locations (Arizona USA, Germany, India, Italy, and Saudi Arabia) observed for over 2 years. Following this, we utilize the extended solar cell parameters (ESPs), a descriptive model for IV characteristics using 10 parameters. The ESPs, then, undergo a principal component analysis (PCA), which transforms the EPSs into a set of uncorrelated principal components (PCs). Individual Gaussian process regressions (GPRs) are then trained on these principal components (PCs). Once the GPRs are trained, the model is capable of reproducing and predicting the complete IV characteristics at any given time t$$ t $$, for specified values of GPOA$$ {G}_{\mathrm{POA}} $$ and TMod$$ {T}_{\mathrm{Mod}} $$. This prediction includes an assessment of its standard deviation, which is derived from data noise and the distance from the observations. This model serves as a versatile tool for various applications, such as analyzing acclimatization effects, degradation trends, seasonal variations, and the performance ratio (PR) of PV modules or systems.

In this study, we present a power-centric enhancement to the photo-current collection efficiency method, introducing the concept of power collection efficiency. The photo-current collection efficiency, defined as the derivative of the cell current with respect to locally generated photo-current, serves as a quantitative metric (ranging from 0 to 1) that effectively measures the efficiency of current transport to the device terminals. Traditionally, photo-current efficiency maps are generated using an electroluminescence imaging technique. Expanding on this method, we propose the generation of efficiency maps that quantify the ratio between locally generated power within the solar cell and the power extracted at the contacts. This new approach not only captures current losses but also incorporates voltage losses, offering a more comprehensive analysis of power losses. To demonstrate the proposed method, we apply it to a silicon heterojunction solar cell under constant illumination, demonstrating its potential for detailed, spatially resolved power loss diagnostics.

In this paper, we introduce a novel method for decomposing Illuminated Lock-In Thermography (ILIT) measurements into spatially resolved and physically meaningful components. These components represent the power dissipation in individual elements of an equivalent circuit model for solar cells. The decomposition process combines a series of differential ILIT images, acquired at various bias points, with a fitted equivalent circuit model derived from the device’s IV characteristics. By correlating the amplitudes of the ILIT images with the corresponding total differential power dissipation predicted by the circuit model, we achieve a linear decomposition of distinct power contributions. This approach identifies a spatial distribution of power dissipation associated with each element in the equivalent circuit. We present the theoretical foundation of our method and demonstrate its application to a silicon heterojunction solar cell. Our results show that ILIT images can be decomposed into components corresponding to the elements in the circuit model, providing a detailed visualization of the spatial distribution of various power contributions.

Individual cell defect counts in electroluminescence (EL) images are often used as evaluation criteria for photovoltaic (PV) plant inspections. However, the significant variation in appearance and quality makes it challenging to extract individual PV cells accurately. To address this, we utilize a high-resolution generative neural network, reducing the problem of cell identification to a more straightforward analysis of a binary grid image. The network is trained on a relatively small (90 images), manually labeled dataset, with augmentations such as cell shuffling significantly improving model performance. We evaluated our method on a dataset of 102000 EL images, including low- and high-resolution images and various module technologies. Our findings show that even a limited number of carefully selected training images, combined with effective augmentations, can achieve a model performance exceeding 95%. Notably, the method remains effective for low-resolution half-cut-cell images, where the separation between module parts is just a few pixels.

Lightweight photovoltaic (PV) modules are able to open up vast new scenarios for PV applications, like building-integrated PV (BIPV) and vehicle-integrated PV (VIPV). Silicon heterojunction (SHJ) solar cells have been recognized as one of the most advanced technologies for improving solar power generation. However, SHJ solar cells are inherently susceptible to damp heat-induced degradation (DHID), which is a critical concern for their application. In this study, lightweight SHJ mini-modules with a low area density (similar to 2 kg/m(2)) while preserving high power density (similar to 70 W/kg) were fabricated using SHJ solar cells with different encapsulation materials and architectures. A comprehensive analysis of the module degradation was carried out, focusing on the optical and electrical properties of the modules and the chemical properties of the encapsulants after 1000 h of accelerated damp heat (DH) aging test. The efficiency loss in lightweight SHJ solar modules after DH test varied significantly, ranging from 3.22 %(rel) to 54.06 %(rel), depending strongly on the encapsulation materials. The increase in series resistance (R-s) was generally the dominant cause of module efficiency degradation. An optimized damp heat-stable lightweight SHJ module was successfully fabricated, with only 0.47 %(rel) efficiency degradation after 1000 h of the DH test. Its stability is almost the same as that of the glass/back sheet module. The comparative study and comprehensive investigation provide insights into the DHID behavior of lightweight SHJ solar modules with different encapsulation materials, contributing to the development of lightweight SHJ solar modules with high DH stability for industrialized mass production.

The influence of data quality on the efficiency of inferences is a fundamental issue in machine learning, and the automatic analysis of photovoltaic (PV) images is no exception. The quality of electroluminescent (EL) images of PV cells often suffers due to economic constraints in large-scale PV plant evaluations. Therefore, the limitations in data quality must be considered when assessing task performance, such as the classification of PV cell defects. This work investigates the effect of data quality on a classification problem by utilizing several large datasets of EL images. Our approach involves simulating degradation in image quality and ground truth, allowing us to measure the impact on classification performance. Our results quantify the decrease in classification performance caused by noise, out-of-focus images, varying resolutions, inaccurate module-to-cell image processing (slicing), and the size of the training dataset. Furthermore, we employ blind, repetitive ground truth labeling to evaluate the impact of human bias, specifically the rate of expert disagreement.

In applications that utilize detailed solar resource assessments with high-resolution topography data, calculating the topographic horizon is critical for accurate shading calculations. In particular, the horizon calculation significantly influences the time needed to model solar irradiation in integrated photovoltaic applications. The new approximate horizon algorithm was developed to balance accuracy and computation time. This study evaluates the algorithm's performance in modeling vehicle- and building-integrated photovoltaics, considering the impact of surface orientation and elevation. It is demonstrated that the proposed horizon algorithm achieves the same level of accuracy four times faster than previously known approaches for vehicle-integrated applications. Moreover, for building-integrated applications, the proposed approach performs better at elevations higher than 10 m on facades and roofs. Finally, the impact of maximum sampling distance on irradiation for high- and low-resolutions topography is studied. The topographic horizon is critical in accurately estimating solar potential, particularly for integrated photovoltaics (PV). A new algorithm for approximating the horizon uses quasirandom sequences, enabling it to achieve the necessary accuracy faster than previous methods. This study evaluates the algorithm's performance in modeling vehicle- and building-integrated PV systems and proposes optimal parameters for computing the horizon.image (c) 2024 WILEY-VCH GmbH

Nonuniformity of irradiation in photovoltaic (PV) modules causes a current mismatch in the cells, which leads to energy losses. In the context of vehicle-integrated PV (VIPV), the nonuniformity is typically studied for the self-shading effect caused by the curvature of modules. This study uncovers the impact of topography on the distribution of sunlight on vehicle surfaces, focusing on two distinct scenarios: the flat-surface cargo area of a small delivery truck and the entire body of a commercial passenger vehicle. We employ a commuter pattern driving profile in Germany and a broader analysis incorporating random sampling of various road types and locations across 17,000 km2 in Europe and 59,000 km2 in the United States using LIDAR-derived topography and OpenStreetMap data. Our findings quantify irradiation inhomogeneity patterns shaped by the geographic landscape, road configurations, urban planning, and vegetation. The research identifies topography as the primary factor affecting irradiation distribution uniformity, with the vehicle's surface orientation and curvature serving as secondary influencers. The most significant variation occurs on vertical surfaces of the vehicle in residential areas, with the lower parts receiving up to 35% less irradiation than the top part of the car. These insights may be used to improve the design and efficiency of vehicle-integrated photovoltaic systems, optimizing energy capture in diverse environmental conditions.

The performance of thin-film solar cell technologies is undermined by partial shading, which induces reverse bias stress, triggering a thermal runaway effect. This condition can cause permanent, shunt-like defects, beginning as local hotspots and escalating into wormlike defects, thereby deteriorating the cell efficiency. Our study employs reverse current ramp experiments on encapsulated Cu(In,Ga)Se-2 thinfilm solar modules. Our objective is to precisely determine the breakdown location and its relationship with the breakdown current and voltage, documenting both thermal and electrical responses during these incidents. This approach enables a better understanding of failure mechanisms under reverse bias conditions.

The topographic horizon can be used to estimate shading for applications requiring an accurate solar resource estimate, including the effects of the local terrain. With high-resolution topography data, horizon estimation significantly contributes to the computation time needed for irradiance simulations. Approximate horizon algorithms have been proposed previously, sampling topography in a finite number of directions. This paper extends this idea by presenting a sampling approach based on the quasirandom sequences. The sampling strategy is optimized with 300,000 km2 of topography data from Europe and the USA. We demonstrate that our algorithm is several orders of magnitude faster than the precise horizon calculation and significantly improves the previous approximate algorithms. In particular, at the ground level with a topography resolution of 1 m/pixel, the proposed method achieved the same accuracy as the old approximate method four times faster. The horizon is one of the key methods in geographical information science and a part of many modeling tools in the built environment. Improvements in computation time benefit all applications that require irradiance modeling, e.g., the yield of vehicle- and building-integrated photovoltaic or temperature modeling of buildings.

Considering warranted lifetimes of PV modules of typically 20-30 years, the performance stability of PV modules is one of the most crucial factors regarding power generation and yield. Long term outdoor performance studies depict realistic operation conditions best, but the correlated nature of e.g. irradiance and temperature impedes the physical interpretation. The possibility to control and tune the conditions in laboratory experiments enables to decompose effects, that are typically overlayed in outdoor experiments. This way, laboratory are crucial to interpret the results obtained in outdoor degradation studies. One driving force of degradation of CIGS modules is light exposure. In literature the focus of light induced degradation (LID) of CIGS modules and solar cells is on metastable changes analyzed on time scales ranging from minutes to hours. In this work we expose industrially produced encapsulated CIGS solar cells for more than 1000 h to light with varied intensity under varied temperature conditions. Such, we aim to study temperature and light intensity dependencies of the observed performance changes. Furthermore, we study the influence of applied bias by comparing LID at short and open circuit. We demonstrate that LID under short circuit conditions leads to VOC degradation, while being temperature assisted and not dependent on the irradiance intensity. CIGS solar cells kept at open circuit conditions appear to be stable under illumination. Exploiting the one diode model, we further connect the observed temperature assisted performance loss to enhanced recombination with lower ideality factor, in comparison to the dominant recombination process before degradation.

This paper proposes a fast and robust method for electroluminescence image preprocessing, where lens and perspective distortions are corrected, and individual cells in the module are detected. Our approach works with low-resolution (640 × 512 pixels) images, uses an image-to-image translation neural network, and leverages the geometric properties of a photovoltaic module. The fast computational speed of the neural network allows us to complete image analysis in under 0.5 seconds, which is ten times faster than currently published methods. In addition, the geometry-based postprocessing makes our approach robust to small misdetections in the neural network output.

When a solar cell is subjected to a negative voltage bias, it locally heats up due to the deposited electrical power. Therefore, every investigation of cell characteristics in the negative voltage regime faces the challenge that the measurement itself changes the state of the cell in a way that is difficult to quantify: On the one hand, the reverse breakdown is known to be strongly temperature dependent. On the other hand, negative voltages lead to metastable device changes which are also very sensitive to temperature. In the current study, we introduce a new approach to suppress this measurement-induced heating by inserting time delays between individual voltage pulses when measuring. As a sample system we use thin-film solar cells based on Cu(In,Ga)Se2 (CIGS) absorber layers. First we verify that with this approach the measurement-induced heating is largely reduced. This allows us to then analyse the impact of the heating on two characteristics of the cells: (i) the reverse breakdown behaviour and (ii) reverse-bias-induced metastable device changes. The results show that minimising the measurement-induced heating leads to a significant increase of the breakdown voltage and effectively slows down the metastable dynamics. Regarding the reverse breakdown, the fundamental tunneling mechanisms that are believed to drive the breakdown remain qualitatively unchanged, but the heating affects the quantitative values extracted for the associated energy barriers. Regarding the reverse-bias metastability, the experimental data reveal that there are two responsible mechanisms that react differently to the heating: Apart from a charge redistribution at the front interface due to the amphoteric (VSe–VCu) divacancy complex, the modification of a transport barrier is observed which might be caused by ion migration towards the back interface. The findings in this study demonstrate that local sample heating due to reverse-bias measurements can have a notable impact on device behaviour which needs to be kept in mind when developing models of the underlying physical processes.

In this work, we present a method to study thermal runaway effects in thin-film solar cells. Partial shading of solar cells often leads to permanent damage to shaded cells and degrades the performance of solar modules over time. Under partial shading, the shaded cells may experience a reverse bias junction breakdown. In large-area devices such as solar cells, this junction breakdown tends to take place very locally, thus leading to very local heating and so-called “hot-spots”. Previously, it was shown that a positive feedback effect exists in Cu(In,Ga)Se2 (CIGS) thin-film solar cells, where a highly localized power dissipation is amplified, which may lead to an unstable thermal runaway process. Furthermore, we introduced a novel characterization technique, laser induced Hot-Spot Lock-In Thermography (HS-LIT), which visualizes the positive feedback effect. In this paper, we present a modified HS-LIT technique that allows us to quantify directly a loop-gain for hot-spot formation. By quantifying the loop-gain we obtain a direct measure of how unstable a local hot-spot is, which allows the non-destructive study of hot-spot formation under various conditions and in various cells and cell types. We discuss the modified HS-LIT setup for the direct measurement of the loop-gain. Furthermore, we demonstrate the new method by measuring the loop-gain of the thermal runaway effect in a CIGS solar cell as a function of reverse bias voltage.

In general, cell development and optimization is performed w.r.t. the cell performance parameters under standard test conditions (STC). However, as the performance of solar cells varies with temperature and irradiation, a single measurement at STC cannot reflect the performance of a device operated in various climatic conditions. In this article, we extrapolate module performance in various climates from cell performance verified at different conditions, i.e., we extrapolate module performance from the cell performance measured under various conditions. This way, and by employing the standardized climatic data provided by the IEC61853 norm, we are able to quickly determine the expected annual yield on module level under a wide variety of climatic conditions. Using this approach, we can assess which cell optimizations harbor the highest potential for yield improvements.

Reverse breakdown in Cu(In,Ga)Se2 (CIGS) solar cells can lead to defect creation and performance degradation. We present pulsed reverse-bias experiments, where we stress CIGS solar cells with a short reverse voltage pulse of ten milliseconds and detect the electrical and thermal response of the cell. This way, we limit the duration of the reverse stress, allowing us to study the initial stages of reverse-bias defect creation in CIGS solar cells and modules. Our results show that permanent damage can develop very fast in under milliseconds. Furthermore, we find the location of defect creation as well as the susceptibility to defect creation under reverse bias depends strongly on whether the cell is encapsulated or not, where encapsulated cells are generally more robust against reverse bias.

The underlying mechanisms of the initial stages of hot-spot and therefore defect creation due to reverse breakdown in Cu(In,Ga)S $\text{e}_{2}$ solar cells are not well understood. We test the thesis, that permanent damage is created due to a positive feedback loop of local temperature enhancing the local current and vice versa, resulting in a thermal runaway. We present experiments on reverse stress with simultaneously introducing local heat. Depending on the temperature profile of the introduced heat, the local current density is enhanced and leads to a gain in the local temperature. This feedback loop is shown to lead to reverse breakdown, causing permanent damage.

Herein, a benchmark dataset for vehicle‐integrated photovoltaics irradiance modeling is proposed. The vehicle trip data consist of trips in the state of North Rhine‐Westphalia in Germany starting from March 2021, which amounts to more than 73 h and a total distance of 3422 km. The sensor box is equipped with GPS, a magnetic compass, acoustic wind, and irradiance sensors and records at a rate of 0.58 Hz. The irradiance sensors are positioned on four sides of the vehicle: roof, left, right, and rear. In addition to the data, a model that uses high‐resolution aerial‐measured topography (LIDAR) and low‐resolution satellite‐based weather data to forecast the effective irradiation of modules mounted on a moving vehicle is discussed. The utility of the simulation approach is demonstrated by computing irradiation over long periods for various driving profiles and comparing results with the collected measurement data. The data are published as a challenge, and the developed software is available in open source.