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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR)

Mohamed Benghanem1, Badis Lekouaghet2, Sofiane Haddad2

  • 1Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, Kingdom of Saudi Arabia. mbenghanem@iu.edu.sa.

Environmental Science and Pollution Research International
|January 24, 2023
PubMed
Summary

A new Quasi-Oppositional Logistic Chaotic Rao-1 (QOLCR) algorithm accurately optimizes photovoltaic (PV) system parameters. This method improves PV module performance prediction and system design by minimizing errors.

Keywords:
ChaoticOptimization algorithmPV parametersPhotovoltaic modulesRao-1 algorithm

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Materials Science

Background:

  • Photovoltaic (PV) module performance prediction is challenging due to nonlinear behavior and missing datasheet information.
  • Accurate characterization of current-voltage (I-V) and power-voltage (P-V) curves is crucial for optimal PV system design.
  • Existing optimization methods may lack the accuracy and robustness required for complex PV systems.

Purpose of the Study:

  • To introduce a novel optimization method for photovoltaic cell/module parameters.
  • To enhance the characterization of I-V and P-V curves for various PV models.
  • To improve the accuracy and robustness of PV system performance prediction.

Main Methods:

  • Incorporation of a chaotic map into the quasi-oppositional Rao-1 algorithm, creating the QOLCR algorithm.
  • Minimization of the root mean square error (RMSE) between estimated and actual PV performance data.
  • Simulation and comparison of the QOLCR algorithm against other optimization methods in single and double diode models.

Main Results:

  • The QOLCR algorithm demonstrated high accuracy and robustness in parameter optimization.
  • Achieved minimal RMSE values of [Formula: see text] for the single diode model and [Formula: see text] for the double diode model.
  • The QOLCR algorithm exhibited faster convergence compared to the basic Rao-1 algorithm and its variants.

Conclusions:

  • The QOLCR algorithm effectively optimizes PV module parameters, enhancing performance prediction.
  • The developed method offers a robust tool for accurate PV system design and configuration.
  • The QOLCR approach provides superior accuracy and efficiency over existing optimization techniques for PV applications.