Abstract
The integration of photovoltaic systems (PVSs) in future power systems grows into a more attractive choice. Thus, the studies related to PVSs operation have gained immense interest. Particularly, research in identifying PV cell model parameters remains an agile field because of the non-linearity of PV cell characteristics and its wide dependency on meteorological conditions of irradiation level and temperature. This paper proposes an Opposition-based Learning Modified Salp Swarm Algorithm (OLMSSA) for accurate identification of the two-diode model parameters of the electrical equivalent circuit of the PV cell/module. Six metaheuristic algorithms, including the recently released basic algorithm SSA, used with the benchmark test PV model of the double diode, and a practical PV module, are employed to assess the performance of OLMSSA. The experimental results and the in-depth comparative study clearly demonstrate that OLMSSA is highly competitive and even significantly better than the reported results of the majority of recently-developed parameter identification methods.
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•A novel OLMSSA algorithm is proposed for parameter extraction of PV cell DDM model.•Improvements are proposed to better consolidate the original SSA’s performance.•The proposed OLMSSA is tested using both simulated and practically measured datasets.•Comparisons with the reported state-of-the-art algorithms are investigated.•The proposed OLMSSA exhibits better accuracy, stability, and convergence speed compared to other methods.