In this paper a distributed-computing technique to extract photovoltaic module parameters is presented. The proposed procedure is based on a particle swarm optimization (PSO) strategy that exploits interlacing of the experimental data in order to decrease the computational load, and parallel computing of different and independent runs to collect a large amount of data in order to save computational time. The results show that the choice of the search range width and the rules to accept/refuse possible solutions strongly influences the parameter extraction and involves exploiting further statistical analysis to control the statistical instability typical of numerical data fitting algorithms.

Parameter extraction of photovoltaic modules with a distributed computing approach

REGGIANI, UGO;SANDROLINI, LEONARDO
2005

Abstract

In this paper a distributed-computing technique to extract photovoltaic module parameters is presented. The proposed procedure is based on a particle swarm optimization (PSO) strategy that exploits interlacing of the experimental data in order to decrease the computational load, and parallel computing of different and independent runs to collect a large amount of data in order to save computational time. The results show that the choice of the search range width and the rules to accept/refuse possible solutions strongly influences the parameter extraction and involves exploiting further statistical analysis to control the statistical instability typical of numerical data fitting algorithms.
2005
Proceedings of the 20th European Photovoltaic Solar Energy Conference and Exhibition
274
277
M. Artioli; U. Reggiani; L. Sandrolini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/3641
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