Spatial Characterization of Sub-Synchronous Oscillations Using Black-Box IBR Models

TL;DR

This paper presents a method to characterize sub-synchronous oscillations in power systems with inverter-based resources using black-box models and spatial visualization, aiding in predicting and mitigating oscillation risks.

Contribution

It introduces a novel approach to estimate black-box IBR models via frequency-domain techniques and uses eigenvector analysis for spatial visualization of oscillation modes.

Findings

Black-box IBR models accurately replicate actual oscillatory behavior.

Spatial heatmaps reveal the spread and severity of SSO modes.

Method effectively identifies susceptible regions in IBR-dominated systems.

Abstract

Power systems with high penetration of inverter-based resources (IBRs) are prone to sub-synchronous oscillations (SSO). The opaqueness of vendor-specific IBR models limits the ability to predict the severity and the spread of SSO. This paper demonstrates that black-box IBR models estimated through frequency-domain identification techniques, along with dynamic network model can replicate the actual oscillatory behavior. The estimated IBR models are validated against actual IBR models in a closed-loop multi-IBR test system through modal analysis by comparing closed-loop eigenvalues, and participation factors. Furthermore, using output-observable right eigenvectors, spatial heatmaps are developed to visualize the spread and severity of dominant SSO modes. The case studies on the 11-bus and 39-bus test systems confirm that even with the estimated IBR models, the regions susceptible to SSO can be identified in IBR-dominated power systems.