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

The evaluation of photovoltaic (PV) model parameters has gained importance considering emerging new energy power systems. Because weather patterns are unpredictable, variations in PV output power are nonlinear and periodic. It is impractical to rely on a time series because traditional power forecast techniques are based on linearity. As a result, meta-heuristic algorithms have drawn significant attention for their exceptional performance in extracting characteristics from solar cell models. This study analyzes a new modification in the double-diode solar cell model (NMDDSCM) to evaluate its performance compared with the traditional double-diode solar cell model (TDDSCM). Modified Fire Hawk Optimizer (mFHO) is applied to identify the photovoltaic parameters (PV) of the TDDSCM and NMDDSCM models. The Modified Fire Hawks Optimizer (mFHO) algorithm, which incorporates two enhancement strategies to address the shortcomings of FHO. The experimental performance is evaluated by investigating the scores achieved by the method on the CEC-2022 standard test suite. The parameter extraction of the TDDSCM and NMDDSCM is an optimization problem treated with an objective function to minimize the root mean square error (RMSE) between the calculated and the measured data. Real data of the R.T.C France solar cell is used to verify the performance of NMDDSCM. The effectiveness of the mFHO algorithm is compared with other algorithms such as Teaching Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Fire Hawk Optimizer (FHO), Moth Flame Optimization (MFO), Heap Based optimization (HBO), and Chimp Optimization Algorithm (ChOA). The best objective function for the TDDSCM equal to 0.000983634 and its value for NMDDSCM equal to 0.000982485 that is achieved by the mFHO algorithm. The obtained results have proved the NMDDSCM's superiority over TDDSCM for all competitor techniques.