Real coded genetic algorithm for optimal parameter estimation of solar photovoltaic model

The modeling of Photovoltaic (PV) cell plays a vital role in evaluating the performance and fault diagnosis of solar PV system. The parameters of the Solar PV model depend on the input parameters namely temperature and irradiance. The parameter estimation of Solar Photovoltaic module is one of the challenging research areas. In this work, the parameter estimation of the PV cell is formulated as an optimization problem and Real Coded Genetic Algorithm is applied to seek the optimal parameter of the solar cell model. The mathematical model of the solar PV cell is expressed explicitly using Lambert function. The estimated PV cell parameters are up scaled to PV module based on the PV module internal cell connection. In the proposed algorithm, the variable of the optimization problem are directly represented as floating point to overcome the drawbacks of representing variables as binary string in the conventional genetic algorithm. Under varying temperature and irradiance conditions, the parameters are estimated and the result is also presented in this paper. The simulation results are compared with binary coded genetic algorithms based parameter estimation in terms of accuracy and computational time in a PC with Core 2 Duo processor with 3 GB RAM.