A comprehensive and critical review of bio-inspired metaheuristic frameworks for extracting parameters of solar cell single and double diode models

Abstract

In today’s world of sustainability and environmentally friendly trends, renewable energy technologies like solar cells play a vital role, making producing the optimal capacity a legitimate aspiration for such technologies’ operators. This article presents an exhaustive and critical review of twenty bio-inspired metaheuristic techniques and their mathematical contexts brought to action by stochastic optimization to identify the parameters of solar cell single diode (SDM) and double diode (DDM) models. The novelty embedded within this appraisal lies in rating these algorithms and their associated variants according to the accuracy of the parameter extraction measured by the root mean square error, which would save effort and time for future researchers and users alike. Additionally, this work outlines a general methodology or pattern followed by every algorithm within the bio class to find the global best solution. Hence, we have concluded: (1) SDM is preferred for solar cell parameter identification. (2) The Firefly algorithm outpaces the other algorithms within the entire class, while the genetic algorithms have mediocre performance, especially with DDM. (3) The Bat algorithm variants are the most developed in terms of performance among other algorithms’ variants. 4) The differential evolution and the Cuckoo search variants have stable performance compared with their standard algorithms. (5) The swarm-intelligence algorithms have the best performance with SDM and DDM compared to the other sub-categories. Lastly, this paper delivers the advantages and disadvantages of the different optimization philosophies reviewed.