AN IMPROVED GREY WOLF OPTIMIZATION TECHNIQUE FOR ESTIMATION OF SOLAR PHOTOVOLTAIC PARAMETERS

Modelling and parameter extraction of the solar cell is difficult for the researcher due to the nonlinear characteristics of voltage and current. Optimization is the best technique through which we can obtain the optimum parameters from a nonlinear model. Recently, there are a number of optimization techniques that are applied for the estimation of the optimum parameter from solar, but still it is not achieved to date. In the present research, we proposed Improved Grey Wolf Optimization (HPSOGWO) for identifying the optimum parameter of a solar cell. Relative error, convergence speed, accuracy, and stability of the final solution are the statistical result which is compared with the particle swarm optimization (PSO) and Grey wolf optimization (GWO) for a single diode model and double diode model of a solar cell. A comparative study reveals that the improved version of the GWO tool provides a more accurate model for the estimation of the optimum parameter of a solar cell with less number of iteration. Hence, we recommended that HPSOGWO is the best optimization tool for providing the perfect promising performance.