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Related Experiment Video

Updated: Sep 13, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Application of an improved pelican optimization algorithm based on comprehensive strategy in PV parameter

Xu Yong¹, Sang Bicong¹, Zhang Yi²

¹College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China.

Scientific Reports

|July 31, 2025

Summary

This summary is machine-generated.

An improved Pelican optimization algorithm (IPOA) enhances photovoltaic model parameter identification. This novel approach improves convergence speed and accuracy, offering superior performance for solar cell parameter estimation.

Keywords:

Cauchy variation strategy Cubic chaotic mapping Mirror reverse learning strategy Pelican optimization algorithm Photovoltaic model Red-tailed eagle optimization algorithm Refraction reverse learning strategy Wilcoxon rank sum test

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Area of Science:

Engineering
Computer Science
Renewable Energy

Background:

Accurate parameter identification is crucial for photovoltaic (PV) model performance.
Existing meta-heuristic algorithms face challenges with convergence speed and accuracy in complex optimization tasks.

Purpose of the Study:

To introduce an improved Pelican optimization algorithm (IPOA) for enhanced parameter identification of PV models.
To leverage comprehensive strategies for improving population diversity, search capability, and local optima avoidance.

Main Methods:

Initialization incorporates cubic chaotic mapping and refraction reverse learning for population diversity.
Global search is enhanced by integrating the red-tailed Eagle optimization algorithm's soaring phase update formula.
Catchy variation and mirror reverse learning strategies are introduced to boost global search and escape local optima.

Main Results:

The IPOA demonstrated superior performance against eight meta-heuristic algorithms on the CEC2022 test functions.
Statistical significance was confirmed using the Wilcoxon rank sum test.
IPOA achieved the smallest mean square error in parameter identification for single-diode and double-diode PV models.

Conclusions:

The proposed IPOA offers significant improvements in convergence speed and accuracy for PV model parameter identification.
IPOA effectively addresses the challenges of local optima and enhances global search capabilities.
This method provides a superior approach for optimizing critical parameters of various solar cell models.