Parameter Estimation of Photovoltaic Models Based on Improvement of Moutain Gazelle Optimizer Algorithm

Diaa Salama^1*Ala Saleh Alluhaidan²Walaa H. Elashmawi³Ibrahim Shawky Farahat⁴Arar Al Tawil⁵Sandy Adel Nabih¹Sahar A. El-Rahman⁶

• ¹Misr International University, Cairo, Egypt
• ²Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
• ³Faculty of Pharmacy, Misr International University, Cairo, Beni Suef, Egypt
• ⁴Luxor University, Luxor, Egypt
• ⁵Applied Science Private University, Amman, Amman, Jordan
• ⁶Benha University, Benha, Qalyubia, Egypt

Accurate and efficient parameter estimation is crucial for modeling photovoltaic (PV) systems, particularly for single-diode, double-diode, and triple-diode models.This paper introduces the improved Mountain Gazelle Optimizer (iMGO), a novel enhancement of the original Mountain Gazelle Optimizer (MGO), designed to overcome challenges such as local optimization entrapment and slow convergence in optimization processes. i_MGO incorporates advanced operators, including gradient search, production, and local escape mechanisms, which synergistically improve the exploration a nd exploitation capabilities of the algorithm.To validate its effectiveness, i_MGO was applied to parameter estimation tasks for the three PV models using an innovative objective function designed to minimize the root mean square error (RMSE) between measured and simulated current values. The results demonstrated the superior performance of i_MGO, achieving minimal RMSE values and reliable convergence, thereby offering significant improvements in both accuracy and computational efficiency. This advancement provides a robust tool for enhancing the design and simulation of photovoltaic (PV) systems, establishing a benchmark for optimization algorithms in renewable energy applications.