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Parameter estimation of static/dynamic photovoltaic models using a developed version of eagle strategy gradient-based optimizer

Abstract:

The global trend towards renewable energy sources, especially solar energy, has had a significant impact on the development of scientific research to manufacture high-performance solar cells. The issue of creating a model that simulates a solar module and extracting its parameter is essential in designing an improved and high performance photovoltaic system. However, the nonlinear nature of the photovoltaic cell increases the challenge in creating this model. The application of optimization algorithms to solve this issue is increased and developed rapidly. In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this. In the literature, different PV models are presented, including static and dynamic PV models. Firstly, in order to evaluate the effectiveness of the proposed ESCGBO algorithm, it is executed on the 23 benchmark functions and the obtained results using the proposed algorithm are compared with that obtained using three well-known algorithms, including the original GBO algorithm, the equilibrium optimizer (EO) algorithm, and wild horse optimizer (WHO) algorithm. Furthermore, both of original GBO and developed ESCGBO are applied to estimate the parameters of single and double diode as static models, and integral and fractional models as examples for dynamic models. The results in all applications are evaluated and compared with different recent algorithms. The results analysis confirmed the efficiency, accuracy, and robustness of the proposed algorithm compared with the original one or the recent optimization algorithms.