Artificial Intelligence (AI) Based Black-Box Modeling Algorithm for System Identification in Three-Phase Single-Stage PV Inverter Systems

With the increasing penetration of solar energy in distribution systems, the precise modeling and appropriate control of photovoltaic (PV) generation systems are becoming increasingly significant. However, the modeling and system identification of inverter-based resources is challenging since sensitive information (e.g., topologies or parameters of electrical components) could not be provided by manufacturers. To address the issue, this paper proposes a data-driven black-box modeling approach using a nonlinear autoregressive exogenous neural network (NARX NN), aiming to estimate the dynamic behaviors of three-phase single-stage PV inverters with a hierarchical control diagram. The proposed method can predict the target output only based on the input and output measurements of the black-box system with unknown topology and parameters. Finally, simulation and experimental results are presented to demonstrate the effectiveness of the proposed work.