Work

Data-driven koopman operator-based detection and location for multi-source forced oscillations

Year: 2025

Type: article

Abstract: The identification and localization of oscillation sources (OSs) is crucial for the effective suppression of forced oscillations in power systems. Herein, we introduce a novel data-driven methodology, leveraging the Koopman operator, for the detection and localization of forced oscillations. The proposed method begins by identifying forced oscillations based on their distinct time-domain characteristics via phase trajectories. A Koopman operator-based modal contribution factor is then defined to quantify the sensitivity of each mode to state variables. The Koopman operator is extracted from measured dynamic data via sparsity promoting dynamic mode decomposition (SpDMD), and the OS locations are determined by computing the modal contribution factor. The accuracy of this method is validated via simulation studies and tests with real-world data, demonstrating its applicability to multisource scenarios.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111473

Authors: Deyou Yang, Runlin Pu, Han Gao, Mingfeng Wang

Institution: Harbin University of Science and Technology

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,920

Funder: Natural Science Foundation of Heilongjiang Province