Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements

TL;DR

This paper introduces a new data-driven method called Extended Subspace Identification (ESI) for characterizing nonlinear power system dynamics using only output measurements from PMUs, even when internal states are unobservable.

Contribution

The paper presents a novel ESI approach tailored for power systems that can identify system dynamics, nonlinear modes, and parameters solely from output measurements, addressing limitations of existing methods.

Findings

Successfully identified nonlinear modes and system parameters

Validated method on multiple network models and scenarios

Effective with realistic noisy measurements

Abstract

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.