Data-Driven Voltage Regulation of Distribution Grid Using Nonlinear Autoregressive Model with Exogenous Inputs (NARX)

This article proposes data-driven control via a nonlinear autoregressive model with exogenous inputs (NARX) for real-time voltage regulation of a modified feeder using reactive power sources. Traditional voltage control strategies rely on rule-based heuristics or optimization techniques, which often require detailed system models and extensive computational resources. The NARX-based controller learns system dynamics from historical data and predicts optimal reactive power dispatch in real-time for voltage correction. The proposed approach is evaluated on a power system feeder model under varying load and network conditions. Simulation results demonstrate that the NARX-based controller achieves improved voltage regulation, offering higher adaptability to system fluctuations. This study highlights the potential of data-driven control for enhancing the reliability of power distribution networks.. Authors: Donge, Vrushabh [ORNL] (ORCID:0000000306062803); Pereira Pinto, Joao [ORNL]; Adib, Aswad [ORNL] (ORCID:000000020997056X); Krishna Moorthy, Radha [ORNL] (ORCID:0000000240169850); Chinthavali, Madhu Sudhan [ORNL] (ORCID:0000000282340943). DOE Contract: AC05-00OR22725