An efficient Equilibrium Optimizer for parameters identification of photovoltaic modules

Area of Science:

• Renewable Energy Systems
• Photovoltaic Technology
• Computational Intelligence

Background:

• Solar photovoltaic (PV) systems are vital for clean energy, with solar cells as their core component.
• Accurate solar cell parameter estimation is essential for optimizing PV system performance, control, and evaluation.
• Existing parameter estimation methods include analytical, optimization-based, and hybrid approaches, each with limitations.

Purpose of the Study:

• To propose an advanced optimization-based algorithm for accurate solar cell parameter estimation.
• To address challenges in solar cell modeling and data scarcity.
• To enhance the performance of the Equilibrium Optimizer (EO) algorithm through Opposition Based Learning (OBL).

Main Methods:

• Developed an Improved Equilibrium Optimizer (IEO) algorithm, integrating Opposition Based Learning (OBL) for enhanced population diversity.
• Applied IEO to estimate parameters for both single diode models (SDM) and double diode models (DDM) of solar cells.
• Compared the proposed IEO algorithm against seven other optimization algorithms using diverse solar cell and PV panel data.

Main Results:

• The IEO algorithm demonstrated superior accuracy in estimating solar cell parameters compared to existing techniques.
• The proposed methodology achieved a lower mean absolute error, indicating higher precision.
• IEO proved effective in reducing computational cost while maintaining high accuracy.

Conclusions:

• The Improved Equilibrium Optimizer (IEO) with Opposition Based Learning (OBL) is a highly effective method for solar cell parameter estimation.
• IEO offers a significant improvement over conventional methods, providing more accurate and efficient solutions.
• The algorithm's robustness was validated across various solar cell models and datasets.