Parameterization of Solar Cell Model Using Multiculture & Hybrid Mutation Based Evolutionary Programming

Abstract

In this paper, parameterization of the single diode model for solar cell has presented. The problem of obtaining the optimal parameter has transformed as an optimization problem where individual absolute error has minimized by hybrid mutation strategy in the Evolutionary programming. Hybridization has given between Gaussian mutation strategy and Cauchy mutation strategy to obtain the better offspring. To increase the reliability of the solution, two stages based a multiculture architecture has proposed. On the first stage, a multi-population strategy has applied to form a multiculture environment, where each population evolved independently to explore the solution domain.This stage will prevent the solution to trap in the local minima. In the second stage, evolved population from first stage combine and members having high fitness are selected to form a new population of the same size as the individual population in the first stage. This second stage population evolved further to meet the final objective. The performance of the proposed method has evaluated over a 57mm diameter commercial solar cell. The obtained performance has compared with results available in current literature where various other approaches like, Levenberg–Marquardt with Simulated annealing, Global Grouping-based Harmony Search, Artificial Bee Swarm Optimization, Chaotic Particle Swarm Optimization, Differential Evolution, etc. have considered. The proposed solution has delivered the minimum error in comparison to other methods and very closer to the experimental data.