English title

Parameter estimation of photovoltaic cell model based on measured characteristics is difficult due to the nonlinear behavior of the diode in the model and its exponential relation. Existence of two diodes in double-exponential model makes this estimation more difficult. A hybrid optimization algorithm is implemented based on explorative characteristic of invasive weed algorithm, probabilistic models of distribution estimation and utilizing dispersion capacities of a mixed Gaussian–Cauchy distribution to estimate parameters of the single and double diode models of photovoltaic cells as an optimization problem. The fitness function is defined as the root mean square error between current-voltage curves of the model output and the measured points. The proposed method is implemented for a practical photovoltaic cell based on measured characteristic and the obtained results are compared with other 8 optimization algorithms. The statistical comparison of the difference between current-voltage curve of the optimized circuit model and the measurements verifies more suitable performance of the implemented algorithm compared with others.