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Integrating a Triplet-triplet Annihilation Up-conversion System to Enhance Dye-sensitized Solar Cell Response to Sub-bandgap Light

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Published on: September 12, 2014

Improving photovoltaic cell parameter calculations through a puffer fish inspired optimization technique.

Manish Kumar Singla^1,2, Jyoti Gupta³, Nijhawan Parag⁴

¹Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India.

|July 26, 2024

Summary

The Puffer Fish (PF) algorithm, inspired by puffer fish behavior, accurately estimates solar photovoltaic (PV) cell parameters. This novel meta-heuristic method outperforms existing algorithms in precision and efficiency for solar energy applications.

Keywords:

Mathematical modeling Modified four-diode model Non-parametric test Parameter estimation Puffer fish

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Area of Science:

Renewable Energy Engineering
Computational Intelligence
Materials Science

Background:

Accurate solar photovoltaic (PV) cell parameter estimation is crucial for expanding solar energy deployment.
Traditional methods struggle with the complex, nonlinear behavior of PV cells.
Meta-heuristic algorithms offer a promising alternative for precise parameter estimation.

Purpose of the Study:

To introduce and evaluate the Puffer Fish (PF) meta-heuristic optimization algorithm for estimating parameters of a modified four-diode PV cell model.
To demonstrate the effectiveness of the PF algorithm inspired by male puffer fish's circular structures.

Main Methods:

The Puffer Fish (PF) meta-heuristic optimization algorithm was developed and applied.
PF algorithm's performance was validated against ten benchmark test functions.
Comparative analysis was conducted against Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS).

Main Results:

The PF algorithm achieved superior performance, obtaining optimal solutions with a minimal error of 7.8947E-08.
Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirmed the PF algorithm's superiority.
Benchmark tests demonstrated consistent outperformance of PF over other compared meta-heuristic algorithms.

Conclusions:

The Puffer Fish (PF) algorithm, inspired by natural phenomena, is highly effective for solar PV cell parameter estimation.
The PF algorithm offers enhanced accuracy and efficiency compared to established meta-heuristic methods.
Further research is recommended to explore the broader applications of the PF algorithm in solar energy and other fields.