A Data-Driven Method to Identify IBRs with Dominant Participation in Sub-Synchronous Oscillations
This paper introduces a data-driven (i.e., model-free) approach to identify which inverter-based resources (IBRs) have dominant participation in poorly damped sub-synchronous oscillations (SSO), to get to the root cause for effective mitigation. An Enhanced Dynamic Mode Decomposition (eDMD) method is proposed that incorporates an appropriate set of observables. Based on time-synchronized data (either simulated or real) from IBR connection points, eDMD directly computes data-driven eigenvectors and participation factors to reveal the role of each IBR in poorly damped SSO. We show the improved accuracy of eDMD over conventional Dynamic Mode Decomposition (DMD) by benchmarking both against actual model-based analysis. We demonstrate this first through a synthetic example and then a case study on the IEEE 39-bus test system with 100% IBR. This data-driven, model-free method offers a powerful tool to foresee and mitigate the risk of IBR-induced SSO in planning (simulated data) and post-event analysis (real data) of SSO events.
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