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.

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.

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

Partial shading can significantly impair the efficiency of thin-film solar cells. When exposed to partial shading, cells within the array tend to become reverse biased, leading to thermal runaway events and the emergence of hotspots. In Cu(In,Ga)Se-2 (CIGS) solar cells such hotspots are also associated with so-called worm-like defects. Both theoretical and experimental studies have shown that in CIGS, a positive-feedback loop leads to instability and thermal runaway events. However, we observe an inconsistency between published simulation results and recently published experimental work. In a recent experimental study, it was shown that under certain conditions, a hotspot develops within 1ms, showing signs of melting of the CIGS in an area with a 5 mu m radius. However, in published simulation results, the time for such high temperatures to develop is in the order of seconds, a discrepancy of three orders of magnitude. In this work, we argue that this discrepancy is explained by the size of the seed defect, demonstrating that the origin of these experimentally observed, fast-developing hotspots is likely microscopic defects. To this end, we developed an electro-thermal finite element model, with very high temporal and spatial resolution. We demonstrate that, assuming a seed defect with a 10nm radius, we can reproduce the experimental results with respect to the size of the defect and the time it took to develop.

Photovoltaics (PV) in onboard vehicle applications adds weight to an electric vehicle (EV), increasing the overall energy consumption. Although the added PV system weight (1.5– 40 kg) is small compared to the vehicle weight (1500–2200 kg), the power generated by PV (55–700 W) is also very small com- pared to the power needed (up to 80–285 kW) to propel an EV, making the effect of additional PV system weight on energy con- sumption a non-trivial topic to analyze. We present a method to study the impact of vehicle onboard PV weight on the energy balance of EVs for different Vehicle-added PV (VAPV) and vehi- cle-integrated PV (VIPV) configurations with eight different PV technologies, using data from vehicle onboard measurement campaigns and simulations. Simulations are carried out for the driving phase of two electric cars (medium and large passenger cars). Our method calculates the energy consumption attributa- ble to the added PV system weight (0.05–1.4 Wh/km) and PV energy yield (0.12–3.12 Wh/km) for a selection of trips. The re- sults of these simulations are expressed through a newly intro- duced parameter called “onboard PV yield factor”, where posi- tive values indicate a net energy gain and negative values indicate a net energy loss of the onboard PV system. Our results show that the onboard PV yield factor for a VAPV configuration can range between -69.1 and 86.9 %, and for a VIPV configuration, between 77.2 and 89.7 %.

Partial shading of Photovoltaic (PV) system is commonly observed in outdoor field conditions specially in the RoadIntegrated Photovoltaic systems. The non-uniform illumination causes mismatch in the electrical output between the cells, which results in an instantaneous effect on power generated and a long-term effect on reliability, it also causes hotspot issues. The objective of this work is to study the partial shading effect on the Road-Integrated PV (RIPV) cells and the soiling model effect under the active and inactive states of Multilevel Bypass (MLB) diodes. In this work, an electrical Simulation Program with Integrated Circuit Emphasis (Ng-SPICE) simulation model has been developed using irradiance model data. This data was collected using a Digital Elevation Model (DEM) called Light Resolution Light detection and Ranging (LidAR). The model was simulated using the Simple Sky Dome Projector (SSDP) software to analyze the impact of different shading conditions and study the effect of MLB diodes on the partial shading RIPV modules and the effect of MLB diodes in the soiling model.

The development of a transient temperature model of photovoltaic (PV) modules is presented in this paper. Currently, there are a few steady-state temperature models targeted at assessing and predicting the PV module temperature. One of the most commonly used models is the Faiman thermal model. This model is derived from the modified Hottel-Whillier-Bliss (HWB) model for flat-plate solar-thermal collector under steady-state conditions and assumes low or no thermal mass in the modules (i.e., short time constants such that transients are neglected, and steady-state conditions are assumed). The transient extension of the Faiman model we present in this paper introduces a thermal mass, which provides two advantages. First of all, it improves the temperature prediction under dynamic conditions. Second, our transient extension to the Faiman model allows the accurate parametrization of the Faiman model under dynamic conditions. We present our model and parametrization method. Furthermore, we applied the model and parametrization method to a 1-year data set with 5-min resolved outdoor module measurements. We demonstrate a significant improvement in temperature prediction for the transient model, especially under dynamic conditions. The Faiman temperature model is a steady-state model, which inevitably is inaccurate under dynamic conditions. In this work, we develop a transient extension of the model with one extra parameter representing the thermal mass of the module. We demonstrate that by taking a short time series of irradiance and wind data (of 5 to 15 min), we can greatly improve the accuracy of the predicted temperature, especially under dynamic conditions. image

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.

We assess the accuracy of two steady-state temperature models, namely, Ross and Faiman, in the context of photovoltaics (PV) systems integrated in vehicles. Therefore, we present an analysis of irradiance and temperature data monitored on a PV system on top of a vehicle. Next, we have modeled PV cell temperatures in this PV system, representing onboard vehicle PV systems using the Ross and Faiman model. These models could predict temperatures with a coefficient of determination (R2) in the range of 0.61-0.88 for the Ross model and 0.63-0.93 for the Faiman model. It was observed that the Ross and Faiman model have high errors when instantaneous data are used but become more accurate when averaged to timesteps of greater than 1000-1500 s. The Faiman model's instantaneous response was independent of the variations in the weather conditions, especially wind speed, due to a lack of thermal capacitance term in the model. This study found that the power and energy yield calculations were minimally affected by the errors in temperature predictions. However, a transient model, which includes the thermal mass of the vehicle and PV modules, is necessary for an accurate instantaneous temperature prediction of PV modules in vehicle-integrated (VIPV) applications. The study evaluated the accuracy of Ross and Faiman steady-state temperature models for vehicle-integrated PV systems, finding that both models had higher accuracy when data was averaged over longer time intervals. While the Faiman model's instantaneous response was not affected by weather conditions due to a lack of thermal capacitance term, the study suggested that a transient model including the thermal mass of the vehicle and PV modules would be necessary for accurate instantaneous temperature predictions in vehicle-integrated PV applications. image

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.

High-quality input data is necessary for calculating performance loss and predicting energy output and the lifetime of photovoltaic (PV) modules. Therefore, filtering PV outdoor data for unplausable measurements is crucial for analyzing PV outdoor performance. Photovoltaic outdoor data consists of electrical measurements (e.g. complete current-voltage (IV-) characteristics or single performance parameters), and meteorological data (e.g. irradiation and temperature). Here, the various electrical and meteorological measurement all constitute different dimensions in the measured data. Currently there is no standard for outdoor PV data filtering. However, commonly data filtering is based on simple thresholds in single dimension of the data. Since thresholding usually results in information loss, we propose using a plausibility filter, which simultaneously considers the various dimensions in the data. To this end we use well known correlations between the various dimensions in the data to compute a measure of plausability by means of the Mahalanobis distance. In this work we demonstrate this concept by combining the solar cell IV parameters, module temperature, and various irradiance measurements (plane-of-array, global-horizontal, and diffuse horizontal irradiance).

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.

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.