Data-Driven Discovery of Multiscale Power System Oscillation Governing Equations Using SINDy-SENDAI
For power system operators, this provides an interpretable, robust method to monitor and forecast electromechanical oscillations in grids with high inverter-based resource penetration.
The paper proposes a hierarchical multiscale framework (SINDy-SENDAI) that combines deep learning with sparse identification of nonlinear dynamics to extract interpretable governing equations for power system oscillations. The method outperforms the state-of-the-art Hankel-DMD method on two real-world datasets (2016 Iberian event and 2021 Italian grid data), accurately reconstructing and predicting system behavior.
Monitoring electromechanical oscillations is crucial for maintaining the stability of modern power systems, particularly in the presence of increasing penetrations of inverter-based resources (IBRs), which introduce new dynamic behaviors. In this work, we propose a hierarchical multiscale framework based on the SINDy-SENDAI algorithm to characterize the transient dynamics captured by wide-area measurements. The proposed deep learning architecture robustly separates low- and high-frequency components embedded in sensor data and incorporates a Sparse Identification of Nonlinear Dynamical Systems (SINDy) module in the latent space to identify parsimonious governing equations. In contrast to conventional deep learning approaches that often produce black-box models with limited interpretability, the proposed framework learns an explicit dynamical representation, enabling physical interpretation, stability assessment, and forecasting of electromechanical oscillations. Given the societal importance of modern power systems, the proposed approach is specifically designed to satisfy key requirements for practical deployment, namely robustness, interpretability, and stable performance under diverse operating conditions. The framework is first validated on the two-area Kundur test system using conventional modal analysis as ground truth and subsequently demonstrated on two real-world datasets: the 2016 Iberian oscillatory event and the 2021 ambient measurements from the southern Italian power grid. The results show that SINDy-SENDAI consistently outperforms the state-of-the-art Hankel-DMD method and that the learned latent dynamics are sufficiently informative to accurately reconstruct and predict the behavior of the full system in the original state space.