A Hybrid TLBO and Simplex Algorithm to Extract the Optimal Parameters of Photovoltaic Models

Abstract

This work aims to improve photovoltaic (PV) system performance by extracting parameters for solar models, on extracting parameters for solar models to enhance the performance of photovoltaic (PV) systems. This paper proposes a hybrid method for figuring out the unknown electrical characteristics of single and dual diode models, integrating Teaching Learning Based Optimization (TLBO) with a simplex algorithm. The primary objective is to achieve optimal parameter extraction for the PV system. To overcome the challenge of local optima, a deterministic algorithm is employed in the hybrid method, leveraging the simplex algorithm’s faster convergence towards optimal parameters. Comparative analysis with other algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), known for handling similar problems, reveals the superior and robust performance of the proposed hybrid approach. The results obtained from the developed method are validated against data from a commercial silicon R.T.C. France solar cell and simulation outcomes under various conditions, further confirming the results’ effectiveness and reliability.