Open AccessModified Salp Swarm Optimization for Parameter Estimation of Solar PV Models
Author(s) -
Mokhtar Yaghoubi,
Mahdiyeh Eslami,
Mohammad Noroozi,
Hamed Mohammadi,
Osman Kamari,
Sivaprakasam Palani
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3213746
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The identification of parameters in solar cell models is still a major challenge in photovoltaic (PV) system simulation and design. Because of its more basic ideas, efficiency, adaptability, swarm and evolutionary optimization algorithms, as well as simple procedural frameworks, have been generally used in industry with real-world problems. However, due to the nonlinearity and complication of the PV parameter identification, the obtained solutions from swarm and evolutionary optimizers were immature. An efficient metaheuristic approach for identifying PV model parameters based on the salp swarm algorithm (SSA) is presented in this paper. In the suggested modified salp swarm optimization (MSSA), the leaders and followers will be updated based on the new formulas. The algorithm’s exploration potential is increased by this modification while also preventing it from converge prematurely. The behavior of the suggested technique is verified using benchmark functions, and the outcomes are contrasted with those of SSA and other successful optimization approaches. The suggested MSSA detects numerous characteristics in the PV model include single diode, double diode, and PV modules, in the most efficient way possible. According to the simulation results, MSSA outperforms the competition and may produce better optimal solutions. The findings demonstrate that the best value of RMSE obtained by MSSA is up to 69 percent lower than other methods and is nearly 5.6 percent lower than that assessed by SSA.