Parameter extraction of solar cell models using chaotic asexual reproduction optimization

To simulate solar cell systems or to optimize photovoltaic (PV) system performance, the estimation of solar cell model parameters is extremely crucial. In this paper, the parameter extraction of solar cell models is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured data. A novel chaotic asexual reproduction optimization (CARO) using chaotic sequence for global search is applied to this parameter extraction problem. All the seven or five parameters of solar cell models are extracted simultaneously using measured input-output data. The CARO has been tested with different solar cell models, i.e., double diode, single diode, and PV module. Comparison simulation results with other parameter extraction techniques show that the CARO signifies its potential as another optional method.