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An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its

Ghareeb Moustafa1, Hashim Alnami1, Sultan Hassan Hakmi1

  • 1Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia.

Biomimetics (Basel, Switzerland)
|October 27, 2023
PubMed
Summary

A new Mantis Search Algorithm (MSA) optimizes photovoltaic (PV) cell models, outperforming existing methods. This novel approach enhances solar energy conversion by accurately estimating PV parameters for improved solar power system performance.

Keywords:
Mantis Search AlgorithmPV model parameters optimisationPV panel characterisationroot mean square error minimisation

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Materials Science

Background:

  • Accurate modeling of solar cell characteristics is vital for photovoltaic (PV) panel performance simulations.
  • Traditional optimization algorithms often suffer from local optima, limiting their effectiveness in complex parameter estimation tasks.
  • Developing novel optimization techniques is crucial for advancing solar energy conversion efficiency.

Purpose of the Study:

  • To introduce the Mantis Search Algorithm (MSA), inspired by praying mantis behavior, for enhanced PV parameter estimation.
  • To evaluate the MSA's performance in modeling R.TC France PV cells and Ultra 85-P PV panels using one-, two-, and three-diode models.
  • To compare the MSA's effectiveness against established optimization algorithms like NNA, DMO, and ZOA.

Main Methods:

  • Development of the Mantis Search Algorithm (MSA) with three distinct optimization stages: prey pursuit, prey assault, and sexual cannibalism.
  • Application of the MSA to estimate parameters for the R.TC France PV cell and the Ultra 85-P PV panel.
  • Comparative analysis of MSA performance against Neural Network Optimization Algorithm (NNA), Dwarf Mongoose Optimization (DMO), and Zebra Optimization Algorithm (ZOA) across six case studies.

Main Results:

  • The MSA demonstrated significant improvements in estimating PV parameters for the one-diode model (1DM), two-diode model (2DM), and three-diode model (3DM).
  • For the R.TC France PV cell, MSA achieved relative improvements over DMO, NNA, and ZOA ranging from 12.4% to 60.13%.
  • For the Ultra 85-P PV panel, MSA yielded substantial gains, with improvements over DMO, NNA, and ZOA ranging from 37.03% to 84.25%.

Conclusions:

  • The proposed Mantis Search Algorithm (MSA) offers a superior approach for optimizing solar cell parameter estimation compared to existing methods.
  • MSA effectively overcomes the limitations of traditional algorithms by avoiding local optima and enhancing simulation accuracy.
  • The developed MSA methodology significantly boosts the electrical characteristics of solar power systems, paving the way for more efficient solar energy utilization.