LAUSR

Abstract Defining the optimal parameters of the photovoltaic system (PV) models according to the actual real voltage and current data is a crucial process during designing, emulating, estimating, dominating, and optimizing photovoltaic systems. Therefore, it is necessary to effectively advance the optimal parameters of the models based on the proper optimization methods. For this purpose, this paper proposes an orthogonal moth flame optimization (MFO) with a local search for identifying parameters of photovoltaic cell models, which is named NMSOLMFO. The presented method is organized based on the principal exploratory and exploitative mechanisms of MFO. Also, its exploration and exploitation capability is strengthened by the orthogonal learning (OL) strategy and Nelder-Mead simplex (NMS) method, and this new scheme supports a more stable equilibrium between the central propensities. In the new MFO-based method, OL strategy can construct a healthier candidate location for the inferior agents, and then, it directs them to probe a reasonable prospective zone throughout a few rational trials. Besides, the NMS local search scheme can augment the accurateness of the global optimal solution by searching its neighborhood throughout the searching process, and the global optimum is taken as the initial point. In our study, first, the developed MFO-based approach is employed to tackle IEEE CEC 2014 benchmark cases with 30D to evaluate the effectiveness of the method in solving high dimensional and multimodal problems. Then, it is utilized to deal with parameters identification of single diode model (SDM), double diode model (DDM), and photovoltaic module model (PVM). The results and statistical studies indicate that NMSOLMFO can outperform the majority of other investigated methods concerning accuracy and convergence rapidity. The obtained results imply that the novel approach can provide a new practical tool for parameter definition in PV models, and it can be beneficial to upgrade the PV systems.