Comparative performance analysis on parameter extraction of solar cell models using meta-heuristic algorithms

Abstract

Optimization of parameters in solar cell modeling allows monitoring the status of the model under different operating conditions of the system and finding possible errors. In order to accurately predict optimal parameters in single and dual diode solar cell models, meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS) and Flower Pollination (FPA) were used. In addition, IAE and RMSE objective functions were used to minimize the error between the experimental diode parameter values calculated by these algorithms. In order to evaluate the accuracy and performance of these algorithms, Genetic algorithm (GA), Simulated Annealing (SA), Harmony Search (HS) and Pattern Search (PS) in the literature were compared numerically and graphically with meta-heuristic algorithms. Comparative results showed that FPA had superior performance in terms of accuracy and reliability compared to other methods in the problem of estimating the parameters of solar cells. Consequently, it was determined that solar cell models were improved by using parameters optimized by meta-heuristic algorithms.