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Dynamic Lévy-Brownian marine predator algorithm for photovoltaic model parameters optimization.

Yassine Bouteraa1, Mohammad Khishe2,3

  • 1Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia. yassine.bouteraa@isbs.usf.tn.

Scientific Reports
|November 25, 2024
PubMed
Summary
This summary is machine-generated.

A new dynamic Lévy-Brownian marine predator algorithm (DLBMPA) improves solar photovoltaic (PV) model accuracy. This enhanced method offers faster convergence and higher precision for PV parameter estimation.

Keywords:
Dynamic Lévy–BrownianMarine predator algorithmPhotovoltaic modelsSolar cell

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Area of Science:

  • Renewable Energy Systems
  • Computational Intelligence
  • Electrical Engineering

Background:

  • Solar photovoltaic (PV) systems present complex characteristics due to their dynamic and multimodal nature, complicating accurate modeling.
  • Traditional optimization methods, like the marine predator algorithm (MPA), face challenges with unpredictable transitions between search mechanisms (Lévy flight and Brownian walk).

Purpose of the Study:

  • To introduce a novel dynamic shift function for the marine predator algorithm (MPA) to enhance its performance in solar photovoltaic (PV) modeling.
  • To develop a more robust and efficient optimization technique for accurate PV parameter estimation.

Main Methods:

  • Development of a dynamic shift function to modulate the interplay between Brownian walk (BW) and Lévy flight (LF) in the MPA.
  • Integration of a constraint handling technique to address parameterization limitations in PV modeling, creating the dynamic Lévy-Brownian MPA (DLBMPA).
  • Comparative performance analysis of DLBMPA against ten established optimization algorithms using various PV models (SDM, DDM, TDM).

Main Results:

  • DLBMPA achieved a statistically significant average RMSE of 9.7 × 10-4 in PV parameter estimation, outperforming ten other algorithms (p < 0.05).
  • The optimized DLBMPA demonstrated high accuracy across different irradiance and temperature levels with an average computation time of 13 ms.
  • DLBMPA exhibited superior speed of convergence and accuracy compared to existing techniques for PV parameter estimation.

Conclusions:

  • The proposed dynamic shift function significantly enhances MPA performance for PV modeling.
  • DLBMPA offers a highly efficient, accurate, and fast solution for estimating solar photovoltaic parameters, overcoming limitations of traditional methods.
  • DLBMPA represents a significant advancement in computational intelligence for reliable solar energy system analysis.