Defect Recognition and Power Loss Estimation Using Infrared Thermography

This study investigates how thermal signatures observed in PV systems using infrared thermography (IRT) affect the performance of PV modules and strings. To assess this, we use IRT images as a basis for circuit modeling of modules with defected solar cells. This allows us to calculate the performance of the individual modules with thermal signatures and the module string. We model the IV characteristics of defective modules based on IRT images and fit the results to field IV traces of the imaged modules. By IRT imaging, several thermal signatures have been identified, and by using a portable IV tracer, the power loss related to each defect under the given conditions has been found. Information from the IRT images are used as input to a model we have developed for defect evaluation, and a comparison of model and IV tracer data for a range of defective solar modules is presented. The string modeling shows that the power loss from a given thermal signature is highly dependent on string length and circuit design. For string sizes larger than 20 modules, only defects that affect two or more module substrings are expected to give a power loss higher than 3%

How much power is lost in a hot-spot? A case study quantifying the effect of thermal anomalies in two utility scale PV power plants

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Defect annealing kinetics in ZnO implanted with Zn substituting elements: Zn interstitials and Li redistribution

It is known that the behavior of residual Li in ion implanted ZnO depends on the preferential localization of the implants, in particular, forming characteristic Li depleted or Li pile-up regions for Zn or O sublattice occupation of the implants due to the corresponding excess generation of Zn and O interstitials in accordance with the so-called “+1 model.” However, the present study reveals that conditions for the radiation damage annealing introduce additional complexity into the interpretation of the Li redistribution trends. Specifically, four implants residing predominantly in the Zn-sublattice, but exhibiting different lattice recovery routes, were considered. Analyzing Li redistribution trends in these samples, it is clearly shown that Li behavior depends on the defect annealing kinetics which is a strong function of the implanted fluence and ion species. Thus, Li depleted and Li pile-up regions (or even combinations of the two) were observed and correlated with the defect evolution in the samples. It is discussed how the observed Li redistribution trends can be used for better understanding a thermal evolution of point defects in ZnO and, in particular, energetics and migration properties of Zn interstitials

Mitigating Snow on Rooftop PV Systems for Higher Energy Yield and Safer Roofs

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Formation and functionalization of Ge-nanoparticles in ZnO

Semiconductor nanocrystals are often proposed as a viable route to improve solar energy conversion in photovoltaics and photoelectrochemical systems. Embedding the nanocrystals in, e.g. a transparent and conducting electrode of a solar cell will promote the photon absorption and subsequent transfer of the generated charge carriers from the nanocrystal, and thereby enhance the function of the electrode. This can be accomplished by embedding a semiconducting nanocrystal with a small bandgap in a transparent conducting oxide (TCO), which is commonly utilized as electrode in new generation solar cells. Here, we demonstrate the incorporation, formation, and functionalization of germanium (Ge) nanocrystals in zinc oxide utilizing ion implantation, where post implantation annealing at 800 °C results in diamond cubic Ge nanocrystals with sizes between 2 and 20 nm. Photoluminecence spectra show a distinct emission around 0.7 eV arising from the Ge nanocrystals, and with additional emission features up to 1.15 eV due to quantum confinement, demonstrating a novel functionalization and tunability of the TCO electrode

Identifying snow in photovoltaic monitoring data for improved snow loss modeling and snow detection

As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations. Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed. In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems

Mitigating Snow on Rooftop PV Systems for Higher Energy Yield and Safer Roofs

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