An online optimization method for extracting parameters of multi-parameter PV module model based on adaptive Levenberg-Marquardt algorithm

Abstract

This paper proposes a novel method to estimate the optimal parameters of a single-diode photovoltaic (PV) module model as well as the active power of PV generation. First, a multi-parameter PV module model is established considering the changes of temperature and solar irradiance; thus, the model is adaptive to different environmental conditions. Second, the model parameter estimation is designed as an optimization problem. An online optimization scheme that only requires the normal operation information is proposed, which avoids the need for an offline experiment to determine the current–voltage (I-V) curve as in conventional PV modeling methods. Third, the adaptive Levenberg-Marquardt (ALM) method with an adaptative damping factor selection strategy is developed to solve the parameter optimization problem, which overcomes the singular issue of traditional LM methods. Moreover, the global sensitivity analysis (GSA) is leveraged to identify critical parameters affecting the PV generation output power, which provides information on model simplification. Finally, the proposed method is tested on data from real-world PV systems in Bend, Oregon, USA, under diverse conditions and scenarios. It is revealed that compared with commonly used methods, the proposed method obtains more accurate PV module parameters and offers PV generation outputs closer to the true outputs.