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Parameter Estimation in photovoltaic systems using a hybrid Bat and crow metaheuristic algorithm.

Abdulsalam Ashour Mohameed Almabrouk1, Selçuk Alparslan Avci1, Javad Rahebi2

  • 1Department of Electrical & Electronics Engineering, Karabuk University, Karabuk, Turkey.

Scientific Reports
|January 6, 2026
PubMed
Summary

A novel hybrid Bat-Crow Search Algorithm optimizes photovoltaic (PV) cell parameter estimation for enhanced solar energy performance. This method improves accuracy in Single, Double, and Triple-Diode Models, outperforming existing techniques.

Keywords:
Bat algorithmCrow algorithmMetaheuristic methodPV panel

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Materials Science

Background:

  • Solar energy adoption is rising, driving demand for efficient photovoltaic (PV) cells.
  • PV cell performance is sensitive to irradiance and temperature, necessitating accurate parameter estimation.
  • Precise parameter estimation is key for effective maximum power point (MPP) tracking.

Purpose of the Study:

  • To develop a hybrid metaheuristic algorithm for optimizing PV parameter estimation.
  • To combine the Bat Algorithm (BA) for global search with the Crow Search Algorithm (CR) for local search.
  • To enhance the accuracy and robustness of PV model parameter identification.

Main Methods:

  • A hybrid metaheuristic algorithm integrating the Bat Algorithm (BA) and Crow Search Algorithm (CR).
  • Application to Single-Diode (SDM), Double-Diode (DDM), and Triple-Diode (TDM) models.
  • Validation using the RTC France dataset and comparison with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

Main Results:

  • The hybrid BA-CR algorithm exhibited superior convergence and robustness.
  • Achieved low Root Mean Square Error (RMSE) values: 0.00077299 (SDM), 0.0008215 (DDM), 0.0008068 (TDM).
  • Demonstrated effective management of parameter uncertainty compared to conventional methods.

Conclusions:

  • The proposed hybrid BA-CR algorithm offers a significant advancement in PV parameter estimation.
  • This approach enhances the accuracy of solar cell modeling for improved energy yield.
  • The method provides a robust solution for optimizing photovoltaic system performance.