This paper presents an analysis of the impact of manufacturing variability in PV modules when interconnected into a large PV panel. The key enabling technology is a compact semiempirical model, that is built solely from information derived from datasheets, without requiring extraction of electrical parameters or measurements. The model explicits the dependency of output power on those quantities that are heavily affected by variability, like short circuit current and open circuit voltage. In this way, variability can be included with Monte Carlo techniques and tuned to the desired distributions and tolerance. In the experimental results, we prove the effectiveness of the model in the analysis of the optimal interconnection of PV modules, with the goal of reducing the impact of variability.

A Semi-Empirical Model of PV Modules Including Manufacturing I-V Mismatch / Chen, Y.; Vinco, S.; Jahier Pagliari, D.; Macii, E.; Poncino, M.. - (2019), pp. 919-922. (Intervento presentato al convegno IEEE International Conference on Electronics Circuits and Systems (ICECS) tenutosi a Genova nel 27-29 November 2019) [10.1109/ICECS46596.2019.8964830].

A Semi-Empirical Model of PV Modules Including Manufacturing I-V Mismatch

Y. Chen;S. Vinco;D. Jahier Pagliari;E. Macii;M. Poncino
2019

Abstract

This paper presents an analysis of the impact of manufacturing variability in PV modules when interconnected into a large PV panel. The key enabling technology is a compact semiempirical model, that is built solely from information derived from datasheets, without requiring extraction of electrical parameters or measurements. The model explicits the dependency of output power on those quantities that are heavily affected by variability, like short circuit current and open circuit voltage. In this way, variability can be included with Monte Carlo techniques and tuned to the desired distributions and tolerance. In the experimental results, we prove the effectiveness of the model in the analysis of the optimal interconnection of PV modules, with the goal of reducing the impact of variability.
2019
978-1-7281-0996-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2769712