Enhanced Optimization Techniques for Parameter Estimation of Single-Diode PV Modules

Abstract

Renewable energy sources such as solar are becoming increasingly popular worldwide. Mathematical derivation is used to show the structural framework of PV cells and their models with a single-diode configuration. This paper proposed a two-step optimization technique for extracting the unknown parameters of solar PV cells and modules. The implementation of the proposed techniques is to find the unknown parameters of the PV module from Kyocera (KC200GT). First, we configured the single-diode configuration of the PV equation in terms of five unknown parameters (Iph, I0, Vt, Rs, and Rsh) and in terms of two unknown parameters (Rs and Vt). After that, we implemented the proposed two-step optimization techniques for extracting the unknown parameters (Iph, I0, Vt, Rs, and Rsh) of the PV module Kyocera (KC200GT). Also, we used the genetic algorithm (GA) and particle swarm optimization (PSO) techniques to find the unknown parameters (Iph, I0, Vt, Rs, and Rsh) of the PV module from Kyocera (KC200GT). The performance of the proposed two-step optimization techniques was compared with the traditional single-stage optimization techniques: particle swarm optimization (PSO), genetic algorithms (GAs), grey wolf optimization (GWO), Villalva’s method, Accarino’s method, Iterative method, and Silva’s method. The test results and the output P–V waveform indicate that the proposed method is more efficient and has a greater impact than the standard techniques.