2025/02/13 by Mohan Du, Du, Mohan, Xiaozhe Wang +1 · 1 voice
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Electrical engineering #Image and Signal Denoising Methods #Systems and Control (eess.SY) #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2502.09592
openalex publication_date 2025/02/13 · arxiv published 2025/02/13 · openalex created_date 2025/10/10 · arxiv updated 2026/05/13 · openalex updated_date 2026/07/28
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.