Parameter extraction of photovoltaic cell/module models using starfish optimization algorithm with a secant-based...

Area of Science:

• Renewable Energy
• Electrical Engineering
• Computational Optimization

Background:

• Accurate photovoltaic (PV) parameter identification is crucial for reliable electrical modeling and energy yield prediction.
• Current methods often focus on algorithms, neglecting objective function formulation improvements.
• Root Mean Square Error (RMSE) is the conventional metric for minimizing discrepancies between measured and estimated PV characteristics.

Purpose of the Study:

• To evaluate the Starfish Optimization Algorithm (SFOA) for PV parameter extraction.
• To propose and validate a novel secant-based objective function reformulation for enhanced accuracy.
• To compare the proposed method against existing optimization algorithms across various PV models.

Main Methods:

• Employing the Starfish Optimization Algorithm (SFOA) for parameter extraction.
• Introducing a secant-based reformulation of the objective function.
• Validating the framework on single-diode (SDM), double-diode (DDM), and three-diode (TDM) models, and PV modules (PVM).
• Utilizing RTC France and Photowatt-PWP201 benchmark datasets for experimental verification.

Main Results:

• The SFOA-Secant configuration achieved superior accuracy across all tested PV models (SDM, DDM, TDM, PVM).
• Significant enhancement in estimation accuracy and robustness was observed by integrating the secant-based objective function.
• The proposed method outperformed competing optimization algorithms in minimizing RMSE values.

Conclusions:

• Reformulating the objective function using the secant method is an effective strategy for improving PV parameter extraction.
• The SFOA-Secant approach offers enhanced accuracy and robustness for PV electrical modeling and performance assessment.
• This work highlights the importance of objective function design in optimization problems for PV parameter identification.