Comparison of different metaheuristic algorithms for parameter identification of photovoltaic cell/module

Abstract

The estimation of the photovoltaic (PV) cell/module model parameters could lead to accomplish a diagnostic tool and to estimate several factors which affect the health state of a PV generator. In this context, it is crucial to look for an extraction technique which performs this evaluation precisely and quickly. Due to the nonlinear and implicit nature of the PV cell/module, significant computational effort is required to obtain all the parameters; therefore, in this context different metaheuristic algorithms are proposed. For the identification of the meaningful parameters of PV cell/module models, illuminated current-voltage (I–V) curves, under real conditions of PV cells temperature and incident irradiance, are employed. Considering several PV cell/module models, the goodness of the proposed algorithms is analyzed by means of statistical errors, convergence speed, and unknown parameters precision. Then these algorithms are tested and validated using a daily set of measured I–V curves, specifically for each one both the whole set of measured data and a reduced set around the maximum power point are used.