Data-Driven $I$–$V$ Feature Extraction for Photovoltaic Modules

Abstract

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.

Neural Network Approach to MPPT Control and Irradiance Estimation

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