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Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification.

Mingjing Wang1,2, Long Chen1,2, Huiling Chen3

  • 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.

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|November 11, 2022
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Summary
This summary is machine-generated.

The multi-strategy learning boosted colony predation algorithm (MLCPA) accurately optimizes photovoltaic parameters for efficient solar energy conversion. This novel approach enhances solar power system performance and reliability.

Keywords:
colony predation algorithmlevel-based learningmulti-strategy learningparameter extractionphotovoltaic models

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Photovoltaic Systems

Background:

  • Accurate modeling of solar systems requires precise identification of photovoltaic parameters.
  • Variable and unknown parameters hinder efficient solar energy conversion.

Purpose of the Study:

  • To introduce the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters.
  • To enhance the efficiency and accuracy of solar power conversion.

Main Methods:

  • Developed MLCPA incorporating opposition-based learning for faster convergence and diversity.
  • Implemented level-based learning for balanced optimization intensity and variation.
  • Validated performance against existing algorithms on benchmark functions.
  • Applied MLCPA to estimate parameters for single, double, and photovoltaic modules.

Main Results:

  • MLCPA demonstrated superior precision and dependability in predicting critical photovoltaic parameters.
  • The algorithm showed enhanced performance compared to current state-of-the-art methods on benchmark functions.
  • Stability analysis confirmed MLCPA's reliability across varying temperatures and irradiance levels.

Conclusions:

  • MLCPA is a precise and dependable tool for solar system parameter identification.
  • The proposed algorithm offers a viable solution for optimizing photovoltaic parameters and boosting solar energy conversion efficiency.