A Modified Particle Swarm Optimization for Parameters Identification of Photovoltaic Models

Parameters identification of solar photovoltaic (PV) models, as a complex nonlinear optimization problem, has received more and more attention of many scholars. Although there have been already numerous techniques for this problem, it is still challenging to identify the 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 in this paper. In MPSO, in order to explore more promising regions of the search space, a mutation operation inspired by differential evolution is employed to improve the quality of personal best of each particle as well as the global best of the current population. Moreover, the damping bound-handling 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 simulation and experimental results comprehensively demonstrate the superiority of MPSO compared to other stateof-the-art algorithms.

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