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

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