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Title: Quantifying and Reducing Curve-Fitting Uncertainty in Isc

Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.
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Resource Relation:
Conference: Presented at the 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 14-19 June 2015, New Orleans, Louisiana; Related Information: Proceedings of the 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 14-19 June 2015, New Orleans, Louisiana
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
Country of Publication:
United States
14 SOLAR ENERGY; 36 MATERIALS SCIENCE Bayesian inference; linear regression; evidence; measurement uncertainty analysis; model discrepancy; noise model; uncertainty quantification; data window selection