Hybrid Tiki Taka and Mean Differential Evolution based Weibull distribution: A comprehensive approach for solar PV modules parameter extraction with Newton-Raphson optimization

Abstract

Effective parameter extraction is crucial in modeling and analyzing complex systems, particularly photovoltaic (PV) cells/modules. Therefore, it is essential to develop a robust optimization model that can effectively determine the optimal parameters of PV models. This study introduces a novel metaheuristic algorithm, Tiki Taka Algorithm (TTA), inspired by sports strategies, particularly the renowned tiki-taka style. The innovation lies in the hybridization of TTA with the Mean Differential Evolution based on Weibull distribution (MDEW). The resultant optimal parameters obtained from this hybridization serve as the initial guess for Newton-Raphson (NR), a critical step that ensures the estimated current closely aligns with the measured current, leading to a minimized Root Mean Square Error (RMSE). The hybrid approach leverages the excellent exploration capabilities of Weibull distribution and the exploitation strength of the Mean Differential Evolution (MeanDE) mutation, offering a comprehensive solution for navigating complex optimization landscapes. The proposed TTA-MDEW effectively identifies various parameters in PV models, including single diodes, double diodes, and PV modules. The findings show that TTA-MDEW holds its ground against the most sophisticated optimization methods in terms of reliability, accuracy, and speed of convergence. Additionally, data from real-world manufacturer datasheets reveal that our method delivers exceptionally precise results under varying irradiance and temperature conditions. Therefore, these outcomes establish the TTA-MDEW as an effective tool for extracting precise parameters from various PV models, enhancing the modeling of PV systems.