Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy

Fast accurate and reliable identification of photovoltaic (PV) model parameters based on measured current-voltage (IV) characteristic curves is significant for the analysis, evaluation and diagnosis of the operating status of in-situ PV arrays to optimize solar energy conversion. Although many techniques have been proposed, it is still challenging to achieve both fast and accurate parameters identification with high reliability. In this paper, based on a new eagle strategy, an improved adaptive Nelder-Mead simplex (NMS) hybridized with the artificial bee colony (ABC) metaheuristic, EHA-NMS, is proposed to improve parameters identification of PV models. The proposed novel eagle strategy consists of three cascaded stages: coarse exploration, coarse exploitation and fine exploitation, through which the strong global exploration of ABC and the powerful local exploitation of NMS merits are combined and the high computation burden of ABC and the high probability of being trapped in local minima of NMS drawbacks are alleviated. The EHA-NMS is compared with some state-of-the-art algorithms on three benchmark problems of model parameters identification of a R.T.C France solar cell and Photowatt-PWP201 PV module which are commonly adopted in the literature. The intensive experiment result and analysis show that the EHA-NMS outperforms other state-of-the-art techniques especially in terms of convergence and reliability. Due to the high computation efficiency, the EHA-NMS can be easily ported to embedded systems to realize online real-time parameters identification of PV models.