Refined photovoltaic parameters estimation via an improved Sinh Cosh Optimizer with trigonometric operators

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Computational Optimization

Background:

• Accurate parameter estimation is vital for photovoltaic (PV) system simulation and design.
• Common solar cell models include single-diode, double-diode, and three-diode configurations.
• Parameter estimation is framed as an optimization challenge to minimize discrepancies between measured and simulated data.

Purpose of the Study:

• To develop and evaluate an enhanced Sinh Cosh Optimizer (I_SCHO) for precise PV parameter estimation.
• To improve the navigation of complex search spaces and convergence speed in optimization algorithms.
• To assess the accuracy and reliability of I_SCHO against existing methods.

Main Methods:

• Integration of trigonometric operators from the Sine Cosine Algorithm (SCA) into the Sinh Cosh Optimizer (SCHO).
• Application of the enhanced I_SCHO algorithm to estimate parameters for five distinct solar cell models.
• Comparative analysis using metrics such as Root Mean Square Error (RMSE), standard deviation (SD), runtime, and convergence behavior.

Main Results:

• The I_SCHO algorithm consistently achieved superior accuracy and reliability in parameter estimation.
• Demonstrated improved ability to avoid local optima and faster convergence compared to other algorithms.
• Validation across multiple solar cell types, including RTC France, Photowatt-PWP201, Kyocera KC200GT, Ultra 85-P, and STM6-40/36 module.

Conclusions:

• The proposed I_SCHO is a highly effective and reliable method for solar cell parameter estimation.
• I_SCHO offers significant advantages in computational efficiency and accuracy for photovoltaic modeling.
• The enhanced algorithm shows strong potential for advancing the design and simulation of solar energy systems.