A Modified Particle Swarm Optimization for Parameters Identification of Photovoltaic Models

Parameters idenfification of solar photovoltaic (FY) models, as a complex nonlinear optimization problem, has received more and more attention of many scholars. Although there have been already numerotis techniques for this problem., it is still challenging to identify tile model parameters accurately. For the purpose of improving the results of parameters identification of different photovoltaic models, a modified particle swarm optimization (MPSO) algorithm is proposed itt this paper. In MPSO, in order to explore more promising regions of the search space, a mutation operation disliked by differential evolution is employed to improve the quality, of personal best of each particle as well as the glolval best of the current population. Moreover, the damping bound-bandling method is used to alleviate the premature convergence, The effectiveness of MPSO is validated "Via estimating parameters of the single diode, double diode, and photovoltaic module model, respectively. The simulattion and experimental results comprehensiyely demonstrate the superiority' of %IPSO compared to other stateof-the-art algorithms.