In research on photovoltaic (PV) device degradation, current-voltage (I-V ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of I-V studies to millions of I-V curves, we have developed a data-driven method to extract I-V curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to I-V curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of I-V data in a short time. Our data-driven feature extraction technique is tested on I-V curves generated with the single-diode model and applied to I-V curves with different data point densities collected from three different sources. This method has a high repeatability for extracting I-V features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard I-V curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple "steps." These features together make the data-driven I-V feature extraction method ideal for evaluating time-series I-V data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.
The authors demonstrate the feasibility of quantifying cell-level performance heterogeneity from module-level I–V curves by determining conditions of bypass diode turn-on. Analysis of these curves falls outside of typical diode-based models of photovoltaic (PV) performance. The authors show that this approach can leverage statistical and machine learning techniques for broad application to massive datasets, and combine those insights with simulations and laboratory-based experiments to provide useful information into the metastability of the interfaces of a PV cell. The authors find good agreement between the experimentally determined curves and the simulated curves, which guide the variable selection in the massive dataset collected from sites in Cleveland, OH, USA, the Negev Desert, Israel, Isla Gran Canaria, Spain, and Mount Zugspitze, Germany.
The x-ray small-angle camera consists of two glass blocks. One of them is used as a monochromator by total reflection, the other shields the plane of observation from background radiation. In connection with a microfocus x-ray tube, this camera offers high intensity and large resolving power. The primary beam is limited only by the width of the microfocus and by the slit between the glass blocks. A comparison has been made with the intensity scattered by a Lupolen sample recorded by a Kratky camera with a quartz monochromator and by our camera. If both cameras are adjusted to the same resolving power (1000 Å), the parasitic scattering is the same for both cameras and the intensity of the glass camera is 4.2 times larger.
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