An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. (1st September 2019) - Chen, Huiling
Graphical abstract: Highlights: ISCA is proposed to identify the parameters of PV models. Multiple learning strategies balance exploration and exploitation. The exploitative trend is boosted with the aid of NMs strategy. The exploratory behavior is enhanced based on OBL strategy. Abstract: Identifying the optimum parameters of photovoltaic models based on the measured current-voltage info is a vital step in monitoring, simulating, and optimizing the photovoltaic systems. We need efficient optimizers to reliably obtain the best model's parameters. In this study, a novel optimizer is proposed to effectively approximate the unknown parameters of solar cells and PV modules. The proposed ISCA is constructed based on the exploratory and exploitative cores of the sine cosine algorithm (SCA). Also, it is enhanced using the Nelder-Mead simplex (NMs) concept and the opposition-based learning (OBL) scheme. In ISCA, NMs method can guarantee the population's intensification and enhance the exploitation ability. In addition, the opposition-based learning scheme can improve the diversification of the population, which ensures a more steady balance between exploitation and exploration trends. The theory and structure of this algorithm are concise; therefore, it can be implemented easily. The developed ISCA is utilized to realize the unidentified parameters of the single diode, double diode, and photovoltaic module. Inclusive results and statistical analyses imply that the ISCA is superior to most of the reported techniques with regard to the accuracy of concluding solutions and convergence ratio. The results indicate that the proposed method can be treated as an effective, promising tool for parameter detection of the solar cells modules in dealing with practical cases.