Inferring power system dynamics from synchrophasor data using Gaussian processes

TL;DR

This paper introduces a Gaussian process-based framework for inferring power system dynamics from synchrophasor data, capable of handling incomplete, heterogeneous, and multi-rate data streams to improve system monitoring and fault detection.

Contribution

It extends Gaussian process methods to multi-input multi-output power system dynamics, enabling continuous-time derivative estimation and uncertainty quantification with minimal system information.

Findings

Accurately infers dynamics at non-metered buses

Imputes and predicts synchrophasors effectively

Locates faults under various disturbance conditions

Abstract

Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system swing dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances.