Improved Extraction of Parameters of Solar PV Cell Diode Model Using Marine Walrus Inspired Optimization Algorithm

Abstract

Accurate parameter estimation is essential for optimizing the performance of solar photovoltaic (PV) models. Traditional methods, such as deterministic approaches, often face challenges due to the inherent nonlinearity of PV systems, resulting in high computational demands and difficulty in accurately extracting key parameters. These methods frequently rely on approximations for the objective functions, which may compromise accuracy. To address these limitations, a novel application of walrus optimization algorithm (WaOA) is introduced for precise parameter extraction in solar PV cell diode models. Inspired by the social and foraging behavior of walruses, WaOA improves performance by globally expanding the search space and incorporates the Newton-Raphson method to refine objective function accuracy. The algorithm is validated on single diode (SDM), double diode (DDM), and triple diode models (TDM), demonstrating superior performance compared to other optimization techniques such as artificial hummingbird optimization (AHO), brown bear optimization (BO), war strategy optimization (WSO), and other hybrid algorithms. The proposed design resulted in faster convergence (about 200 iterations) and significant RMSE improvements of minimum 22.2%, 42.8%, and 29.8% for SDM, DDM, and TDM respectively compared to mentioned optimization approaches. The proposed design is validated across all diode models under various irradiance conditions, confirming its robustness and adaptability.

Keywords

• Diode models
• optimization
• parameter estimation
• root mean square error (RMSE)
• walrus optimization algorithm