Hybrid Young's double-slit experiment and differential evolution for enhanced photovoltaic parameter estimation

Area of Science:

• Renewable Energy Technology
• Computational Intelligence
• Optics

Background:

• Accurate modeling of photovoltaic (PV) systems is crucial for efficient solar energy integration.
• Existing parameter estimation methods may lack the precision required for complex PV models.
• Hybrid optimization techniques offer potential for enhanced performance in scientific modeling.

Purpose of the Study:

• To introduce and evaluate a novel hybrid optimization method, the Young's Double-Slit Experiment-Differential Evolution (YDSE-DE) technique.
• To enhance the precision of parameter estimation in single, double, and three-diode photovoltaic models.
• To demonstrate the superiority of the YDSE-DE algorithm over existing optimization methods in PV modeling.

Main Methods:

• Integration of the Young's Double-Slit Experiment (YDSE) principles with Differential Evolution (DE) algorithms.
• Comparative assessment of the YDSE-DE algorithm against Ant Lion Optimizer (ALO) and Sooty Tern Optimization Algorithm (STOA).
• Rigorous testing and validation across single-diode, double-diode, and three-diode PV models.

Main Results:

• The YDSE-DE algorithm achieved superior accuracy and computational efficiency compared to ALO and STOA.
• Significant improvements in root mean square error (RMSE) were observed across all tested PV models.
• Specific RMSE values: 0.0059117 (single-diode), 0.0015218 (double-diode), and 0.0018409 (three-diode).

Conclusions:

• The YDSE-DE algorithm presents a significant advancement in photovoltaic parameter estimation.
• This hybrid method enhances the precision and effectiveness of solar energy system simulation and design.
• The approach offers potential for optimizing other complex systems requiring reliable simulations.