An Online Data-Driven Method to Locate Forced Oscillation Sources from Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)

TL;DR

This paper presents an online, data-driven method using Sparse Identification of Nonlinear Dynamics (SINDy) to accurately locate forced oscillation sources in power systems without requiring model information, suitable for real-time use.

Contribution

The paper introduces a novel online SINDy-based approach for locating forced oscillation sources in power systems, requiring no prior model data and demonstrating high accuracy in simulations and real events.

Findings

Accurately locates oscillation sources in most cases.

Effective under resonance and poorly damped modes.

Low tuning and computational cost for online application.

Abstract

Forced oscillations may jeopardize the secure operation of power systems. To mitigate forced oscillations, locating the sources is critical. In this paper, leveraging on Sparse Identification of Nonlinear Dynamics (SINDy), an online purely data-driven method to locate the forced oscillation is developed. Validations in all simulated cases (in the WECC 179-bus system) and actual oscillation events (in ISO New England system) in the IEEE Task Force test cases library are carried out, which demonstrate that the proposed algorithm, requiring no model information, can accurately locate sources in most cases, even under resonance condition and when the natural modes are poorly damped. The little tuning requirement and low computational cost make the proposed method viable for online application.