A Model-Agnostic Method for PMU Data Recovery Using Optimal Singular Value Thresholding

TL;DR

This paper introduces a fast, model-agnostic low-rank matrix estimation method using optimal singular value thresholding for recovering noisy and missing PMU data, applicable both offline and online.

Contribution

It proposes a novel, fast, model-agnostic recovery algorithm leveraging optimal singular value thresholding for PMU data with missing entries.

Findings

Accurately recovers signals with noise and missing data.

Works effectively for both offline and online data recovery.

Validated on IEEE 39-bus system and real utility data.

Abstract

This paper presents a fast model-agnostic method for recovering noisy Phasor Measurement Unit (PMU) datastreams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMUchannels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop as well as simultaneous data erasure across multiple PMU channels.