A Novel Metaheuristic Jellyfish Optimization Algorithm for Parameter Extraction of Solar Module

Abstract

Accurate parameter identification plays an integral role in the modeling of an optimized solar module which in turn helps in the error-free prediction of its output. Five important parameters: the photoelectric current (Iph), ideality factor(α), saturation current (Io), series resistance (Rs), and shunt resistance (Rsh) are required for the accurate modeling of the PV cells/modules and need to be extracted as these parameters are not provided by the manufacturer in the datasheet. This paper proposes a new metaheuristic jellyfish optimization (JFO) algorithm for the parameter extraction of a solar module. The JFO algorithm achieves the optimal solution without being trapped in local solutions in less time. The parameter extraction using the JFO algorithm is done on two different solar modules i.e., Soltech-1STH-215P and PWP-201. The results are compared in terms of extracted parameters (Iph, α, Io, Rs, and Rsh) with the well-known optimization techniques like PSO, GA, and others available in the literature and with the manufacturer I–V and P–V characteristics. The proposed technique I–V and P–V characteristics are validated at different environmental conditions and are found to be similar to that of PSO and GA. It is also observed that the extracted parameters obtained using JFO are comparable with the other twenty-two techniques, and the proposed technique is one of the highly efficient techniques that can be utilized for parameter extraction of PV modules and to predict solar cell characteristics for all commercial modules without setting up any experimental measurements. MATLAB/simulation software is used for implementation and performance validation.

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

“…Te total module current is calculated by using equation (1), where I pv is the module current, V pv is the module voltage, I ph is the photogenerated current, I 0 is the diode reverse saturation current, I sh is the current through shunt resistance, R s is the series resistance, R sh is the shunt resistance, N s is the number of cells, V t is the junction's thermal voltage and can be expressed as V T � (k * α * T)/q, where k is the Boltzmann's constant 1.38 × 10 −2 J/K, q is the electron charge equal to 1.602 × 10 -19 C, T is the temperature in kelvin, and α is the ideality factor constant [44], and…”

“…Diferent algorithms can fnd the GMPP at STC, under step-changing irradiation, and PSC [8-20, 23-27, 30, 37-43]. Artifcial jellyfsh optimization (JFO) [44] is a new novel swarm-based optimization algorithm with some advantages over other algorithms. JFO can balance exploration and exploitation strategies well and reach optimal solutions in less time [32,44].…”

“…Artifcial jellyfsh optimization (JFO) [44] is a new novel swarm-based optimization algorithm with some advantages over other algorithms. JFO can balance exploration and exploitation strategies well and reach optimal solutions in less time [32,44]. A comparative analysis is performed for the two panels for STC, step-change of irradiances, and PSC conditions.…”

“…. Karaboga proposed the artifcial bee colony (ABC) method to optimize numerical challenges in 2005 [17,44]. Te ABC algorithm is a swarmbased metaheuristic algorithm.…”

“…Tis will help the readers to select the suitable MPPT methods. Te authors in [44] focused on the use of jellyfsh search optimizer in solving optimization problems. Te authors in [32] have given a detailed explanation of the jellyfsh optimization algorithm with its application in engineering.…”