Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

Area of Science:

• Renewable Energy Systems
• Photovoltaic (PV) Technology
• Optimization Algorithms

Background:

• Increasing global demand for solar energy necessitates precise parameter extraction for photovoltaic (PV) systems.
• Accurate PV model parameters are crucial for optimizing system performance under diverse environmental conditions.
• Existing metaheuristic algorithms have limitations in achieving high accuracy for PV parameter extraction.

Purpose of the Study:

• To enhance the accuracy of parameter extraction for photovoltaic (PV) system models.
• To introduce a novel optimization algorithm, the enhanced prairie dog optimizer (En-PDO), for PV parameter estimation.
• To evaluate the performance of En-PDO against existing algorithms using various solar cell models and benchmark functions.

Main Methods:

• Development of the enhanced prairie dog optimizer (En-PDO) by integrating random learning and logarithmic spiral search into the prairie dog optimizer (PDO).
• Application of En-PDO to primary PV models (single diode, double diode, three diode) and PV module models.
• Validation using experimental datasets from R.T.C. France silicon and Photowatt-PWP201 solar cells, and CEC2020 test functions.

Main Results:

• En-PDO demonstrated superior performance compared to the original PDO and eighteen other recent optimization algorithms.
• The algorithm achieved competitive or superior root mean square error (RMSE) values across different solar cell models.
• En-PDO showed consistent efficacy in accurately modeling diverse solar cell behaviors and optimal performance on CEC2020 test functions.

Conclusions:

• The enhanced prairie dog optimizer (En-PDO) is a robust and reliable method for precise parameter estimation in solar cell models.
• En-PDO offers significant advancements over existing algorithms for photovoltaic system modeling and optimization.
• This approach holds strong potential for improving the efficiency and accuracy of solar energy conversion technologies.