Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

Abstract

In the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode‐based models, such as single‐diode model (SDM), double‐diode model (DDM), and the three‐diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

“…In Ref. 43 , the authors proposed a method for adjusting the force control parameters of the WSO by including a chaotic generator to enhance the exploitation capabilities of the algorithm. Further, the authors in Ref.…”

Optimal Identification for Dynamic PV Cell Parameter Based on a Data-Extension-Driven Method

“…Furthermore, the continuous mutation strategy of ELPSO [30] can effectively enhance the global search ability and avoid prematurity. Besides, there are many other metaheuristic algorithms for parameter identification of PV cell, such as the backtracking search algorithm [31], bald eagle search [32], squirrel search algorithm [33], queuing search optimization [34], white shark optimization algorithm [35], and moth flame algorithm [36] (iii) Machine Learning. In order to evaluate the generation characteristics of PV cell under different weather conditions, a feed-forward neural network [37] was adopted to approximate the parameters by taking the irradiation and temperature as the network input.…”