Evaluation of modified fire hawk optimizer for new modification in double diode solar cell model

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Computational Intelligence

Background:

• Photovoltaic (PV) model parameter evaluation is crucial for emerging energy systems.
• Traditional linear forecasting methods are inadequate due to unpredictable, nonlinear PV output variations.
• Meta-heuristic algorithms excel at extracting characteristics from solar cell models.

Purpose of the Study:

• To analyze a novel modified double-diode solar cell model (NMDDSCM).
• To evaluate the performance of the NMDDSCM against the traditional double-diode solar cell model (TDDSCM).
• To apply the Modified Fire Hawk Optimizer (mFHO) for accurate PV parameter extraction.

Main Methods:

• The Modified Fire Hawk Optimizer (mFHO), with two enhancement strategies, was employed.
• Parameter extraction was framed as an optimization problem minimizing root mean square error (RMSE).
• The performance was validated using real solar cell data (R.T.C France) and the CEC-2022 test suite.

Main Results:

• The mFHO algorithm achieved RMSE values of 0.000983634 for TDDSCM and 0.000982485 for NMDDSCM.
• The NMDDSCM consistently outperformed the TDDSCM across all tested competitor algorithms.
• mFHO's effectiveness was benchmarked against TLBO, GWO, FHO, MFO, HBO, and ChOA.

Conclusions:

• The proposed NMDDSCM demonstrates enhanced accuracy in photovoltaic parameter estimation.
• The mFHO algorithm proves effective and superior for optimizing solar cell models.
• The findings support the NMDDSCM's suitability for advanced new energy power systems.