Data-driven Forced Oscillation Localization using Inferred Impulse Responses

TL;DR

This paper introduces a data-driven method to locate forced oscillation sources in power systems by inferring impulse responses from ambient data and analyzing FO events in the frequency domain, without relying on system models.

Contribution

It presents a novel framework that infers impulse responses from ambient data and uses them for FO source localization, validated on realistic power system models.

Findings

Effective in realistic power system scenarios

Applicable with partial sensor coverage

Validated on IEEE benchmark systems

Abstract

Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems. In this work, we propose a comprehensive data-driven framework for inferring the sources of forced oscillation (FO) using solely synchrophasor measurements. During normal grid operations, fast-rate ambient data are collected to recover the impulse responses in the small-signal regime, without requiring the system model. When FO events occur, the source is estimated based on the frequency domain analysis by fitting the least-squares (LS) error for the FO data using the impulse responses recovered previously. Although the proposed framework is purely data-driven, the result has been established theoretically via model-based analysis of linearized dynamics under a few realistic assumptions. Numerical validations demonstrate its applicability to realistic power systems including nonlinear, higher-order dynamics with control effects using the IEEE 68-bus system, and the 240-bus system from the IEEE-NASPI FO source location contest. The generalizability of the proposed methodology has been validated using different types of measurements and partial sensor coverage conditions.