Parameter identification of the PV systems based on an adapted version of human evolutionary optimizer

Area of Science:

• Renewable Energy
• Solar Photovoltaics
• Computational Optimization

Background:

• Accurate modeling of photovoltaic (PV) cells and modules is crucial for solar energy systems.
• Optimization algorithms offer a promising approach to parameter approximation for PV modules.
• Existing methods face challenges in achieving high precision for PV system parameter estimation.

Purpose of the Study:

• To develop an optimization pipeline for precise parameter estimation in PV systems.
• To apply a novel metaheuristic algorithm, the Adapted Human Evolutionary Optimizer (AHEO), for PV parameter modeling.
• To minimize the root mean square error (RMSE) between simulated and measured I-V curves of PV systems.

Main Methods:

• Implementation of an optimization pipeline for PV system parameter identification.
• Utilizing the Adapted Human Evolutionary Optimizer (AHEO), a metaheuristic algorithm.
• Validation of the proposed method on a commercial PV module.

Main Results:

• The AHEO-based method demonstrated high accuracy in modeling PV module parameters.
• Achieved a significant reduction in root mean square error (RMSE) by 34.6% compared to conventional methods.
• The optimization pipeline effectively minimized discrepancies between predicted and measured I-V curves.

Conclusions:

• The proposed optimization pipeline with AHEO is an effective approach for accurate PV system parameter modeling.
• AHEO offers superior performance in minimizing RMSE for PV module characterization.
• This research contributes to enhancing the reliability and efficiency of solar energy systems through improved modeling techniques.