A Data-Driven Method to Identify Major Contributors to Low-Frequency Oscillations

TL;DR

This paper introduces a data-driven approach using Koopman operator theory and modal analysis to identify power generation plants that contribute to low-frequency oscillations, aiding in system stability analysis.

Contribution

It develops a novel method that leverages PMU data and Koopman analysis to pinpoint oscillation sources without relying on detailed system models.

Findings

Effective identification of oscillation contributors demonstrated on IEEE and WECC test systems.

Validated with real PMU data from ISO-New England system.

Outperforms traditional model-based approaches in post-event analysis.

Abstract

We present a purely data-driven method to pinpoint generation plants that significantly contribute to poorly damped oscillations as part of post-event analysis. First, Extended Dynamic Mode Decomposition (EDMD) is applied on PMU data from the point of interconnection (POI) of the plants to obtain the finite-dimensional Koopman operator. Then, modal analysis is performed on a reduced-order Koopman operator to extract spatio-temporal patterns. The data-driven eigenvalues and eigenvectors quantify each plant's contribution to critical oscillatory modes without requiring any system model information. We demonstrate the effectiveness of this method through simulated case studies on modified IEEE 39-bus and WECC 179-bus test systems by benchmarking the data-driven results against ground-truth models. Its performance is further validated using PMU data from real oscillation events in the ISO-New England system. This data-driven method offers a practical tool for both planning-stage simulations and post-event analysis of real oscillation events, enabling effective mitigation.