A Data-Driven Method for Microgrid System Identification: Physically Consistent Sparse Identification of Nonlinear Dynamics

TL;DR

This paper introduces PC-SINDy, a data-driven method that accurately identifies microgrid dynamics using PMU data, ensuring physically consistent models even with noisy and limited data.

Contribution

The paper presents a novel PC-SINDy approach that leverages an analytically derived function library for precise, physically consistent microgrid system identification.

Findings

PC-SINDy accurately predicts frequency trajectories under large disturbances.

The method works reliably with noisy, low-sampled PMU data.

It generalizes well to scenarios not seen during training.

Abstract

Microgrids (MGs) play a crucial role in utilizing distributed energy resources (DERs) like solar and wind power, enhancing the sustainability and flexibility of modern power systems. However, the inherent variability in MG topology, power flow, and DER operating modes poses significant challenges to the accurate system identification of MGs, which is crucial for designing robust control strategies and ensuring MG stability. This paper proposes a Physically Consistent Sparse Identification of Nonlinear Dynamics (PC-SINDy) method for accurate MG system identification. By leveraging an analytically derived library of candidate functions, PC-SINDy extracts accurate dynamic models using only phasor measurement unit (PMU) data. Simulations on a 4-bus system demonstrate that PC-SINDy can reliably and accurately predict frequency trajectories under large disturbances, including scenarios not encountered during the identification/training phase, even when using noisy, low-sampled PMU data.