Optimal Parameter Estimation Techniques for Enhanced Performance of Solar PV Cell

The clean and abundant availability of solar energy has attracted significant interest from researchers worldwide. This growing focus on photovoltaic (PV) panels has increased the need to develop precise models of solar PV modules before installation. However, the manufacturer's data sheet for a PV module often lacks key parameters necessary for accurate modelling. Consequently, efficient parameter estimation techniques are essential to determine these unknown parameters. Traditional estimation methods often fail to achieve high accuracy, prompting increased interest in bio-inspired optimization techniques for improving PV parameter estimation. Accurate estimation of PV cell parameters is crucial for proper sizing and performance evaluation of solar cells. This paper comprehensively analyzes and compares three different optimization techniques: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Quantum Evolutionary Algorithm (QEA), for estimating PV cell parameters. These algorithms aim to approximate critical parameters of a PV model, including the diode ideality factor, series resistance, shunt resistance, and photo-generated current. The performance of each method is evaluated based on convergence speed, estimation accuracy, and computational complexity.