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An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation.

Ghareeb Moustafa1, Hashim Alnami1, Ahmed R Ginidi2

  • 1Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, P.O. Box114, Jazan, 45142, Saudi Arabia.

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Summary
This summary is machine-generated.

An Improved Kepler Optimization Algorithm (IKOA) enhances photovoltaic (PV) module parameter identification for accurate solar power estimation. This novel method significantly outperforms standard algorithms, improving efficiency and robustness in PV system analysis.

Keywords:
Kepler optimization algorithmLocal Escaping OperatorPV parameters extractionPractical solar modules

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Area of Science:

  • Renewable Energy Systems
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Accurate photovoltaic (PV) module characteristic identification is crucial for optimal solar power estimation.
  • The standard Kepler Optimization Algorithm (KOA) has been used for parameter extraction of the PV triple diode model (3DM).
  • Limitations in the standard KOA's success rate and efficiency necessitate improvements.

Purpose of the Study:

  • To develop and assess an Improved Kepler Optimization Algorithm (IKOA) for enhanced PV module parameter identification.
  • To improve the search process and evade local optima in parameter estimation.
  • To validate the IKOA's performance against the standard KOA and other state-of-the-art algorithms.

Main Methods:

  • The study introduces an Improved Kepler Optimization Algorithm (IKOA) by integrating a Local Escaping Operator (LEO) mechanism.
  • The IKOA and standard KOA are applied to extract nine parameters for the PV triple diode model (3DM).
  • Parameter extraction is performed for three distinct PV modules: Photowatt PWP201, R.T.C France, and STM6-40/36.

Main Results:

  • The proposed IKOA demonstrates significant average improvement rates of 62.27%, 55.1%, and 32.12% for the tested PV modules.
  • IKOA shows superior performance and robustness compared to previously reported methods.
  • The LEO mechanism effectively enhances the search process by evading local optima.

Conclusions:

  • The developed IKOA provides a more effective and robust method for identifying PV module characteristics.
  • This advancement contributes to more accurate PV power estimation in solar systems.
  • The IKOA represents a significant improvement over existing optimization techniques for PV parameter extraction.