PMU-based Voltage Instability Detection through Linear Regression

TL;DR

This paper proposes a linear regression-based preprocessing method for PMU data to improve the detection of voltage instability, validated through simulations and real measurements, enhancing power system stability monitoring.

Contribution

It introduces a novel linear regression approach to preprocess PMU data for more accurate voltage instability detection, addressing measurement noise issues.

Findings

Improved detection accuracy with the proposed method.

Validated effectiveness using real-time simulation and actual PMU data.

Enhanced robustness against measurement errors.

Abstract

Timely recognition of voltage instability is crucial to allow for effective control and protection interventions. Phasor measurements units (PMUs) can be utilized to provide high sampling rate time-synchronized voltage and current phasors suitable for wide-area voltage instability detection. However, PMU data contains unwanted measurement errors and noise, which may affect the results of applications using these measurements for voltage instability detection. The aim of this article is to revisit a sensitivities calculation to detect voltage instability by applying a method utilizing linear regression for preprocessing PMU data. The methodology is validated using both real-time hardware-in-the-loop simulation and real PMU measurements from Norwegian network.