| Summary: | Photovoltaic (PV) cell parameter identification is of great significance to accurate PV cell modelling, which can further critically influence overall optimal control and output characteristics simulation design of PV systems. Nevertheless, this high non-linearity obstacle often simultaneously exists multiple local optimums, thus conventional optimization approaches can hardly maintain a consistently satisfactory performance to obtain global optimum. Hence, an adaptive compass search (ACS) algorithm is employed in this paper to identify several critical unknown parameters of the most common utilized PV cell model, i.e., double diode model (DDM). Compared with fixed sequence based original compass search (CS) algorithm, ACS algorithm can dramatically improve global exploration ability via adaptive sequence of exploration directions via historical searching results. Particularly, case studies verify the feasibility and merits of ACS algorithm, which validates that it can achieve more desirable performance compared against whale optimization algorithm (WOA) in terms of optimization precision and convergence rate.
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