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Updated: Oct 4, 2025

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Photovoltaic models parameter estimation via an enhanced Rao-1 algorithm.

Junhua Ku1, Shuijia Li2, Wenyin Gong2

  • 1School of Science, Qiongtai Normal University, Haikou, 571127, China.

Mathematical Biosciences and Engineering : MBE
|February 9, 2022
PubMed
Summary
This summary is machine-generated.

An enhanced Rao-1 algorithm improves photovoltaic (PV) model parameter estimation accuracy and reliability. This new method, ERao-1, offers superior performance over existing algorithms for various PV models.

Keywords:
Parameter estimationRao-1 algorithmparameter extractionphotovoltaic model

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Area of Science:

  • Renewable Energy Systems
  • Electrical Engineering
  • Computational Intelligence

Background:

  • Accurate photovoltaic (PV) model parameters are crucial for PV power system efficiency.
  • Estimating these parameters is challenging due to the complex equations governing PV models.

Purpose of the Study:

  • To propose an enhanced Rao-1 (ERao-1) algorithm for accurate and reliable estimation of unknown parameters in PV models.
  • To improve upon existing parameter estimation techniques for PV modeling.

Main Methods:

  • Development of a repaired evolution operator within the Rao-1 algorithm.
  • Introduction of a novel evolution operator to avoid local optima.
  • Implementation of a population size linear reduction strategy for adaptive population management.

Main Results:

  • The ERao-1 algorithm demonstrated superior performance compared to existing methods.
  • Achieved high accuracy in parameter estimation for double diode and three diode PV models.
  • Validated accuracy through fitting curves comparing simulated and measured data.

Conclusions:

  • The proposed ERao-1 algorithm is effective for PV model parameter estimation.
  • ERao-1 offers enhanced accuracy and reliability, particularly for complex PV models.
  • The algorithm provides a robust solution for improving PV system performance analysis.