Identification of static and dynamic parameters of PV models through multi-objective optimization

The accuracy of photovoltaic models used to de- scribe the characteristics of photovoltaic panels is crucial to evaluate the performance and to ensure safety, cost-effectiveness and compliance of photovoltaic systems. In particular, when the parameters for static and dynamic models are optimized independently, it has been shown that they might be not consistent across different environmental conditions. By exploiting the dependency of common parameters between the two models, the fitting problem is transformed into a multi-objective optimization problem, which is solved using evolutionary algorithms. The results showed that selecting a tradeoff solution from the Pareto front obtained, leads to a more consistent set of parameters for each environmental condition analyzed.