LASSO-Based Multiple-Line Outage Identification In Partially Observable Power Systems

TL;DR

This paper introduces a LASSO-based framework for identifying multiple-line outages in power systems using partial voltage measurements, improving detection accuracy under limited observability.

Contribution

It presents a novel method combining AC power flow modeling and sparse regression to detect multiple outages with partial PMU coverage, outperforming existing approaches.

Findings

Achieves 93% accuracy for single-line outages at 25% PMU coverage.

Achieves 80% accuracy for double-line outages at 50% PMU coverage.

AC power flow captures outage patterns more effectively than other models.

Abstract

Phasor measurement units (PMUs) create ample real-time monitoring opportunities for modern power systems. Among them, line outage detection and identification remains a crucial but challenging task. Current works on outage identification succeed in full PMU deployment and single-line outages. Performance however degrades for multiple-line outage with partial system observability. We propose a novel framework of multiple-line outage identification using partial nodal voltage measurements. Using alternating current (AC) power flow model, phase angle signatures of outages are extracted and used to group lines into minimal diagnosable clusters. Identification is then formulated into an underdetermined sparse regression problem solved by lasso. Tested on IEEE 39-bus system with 25% and 50% PMU coverage, the proposed identification method is 93% and 80% accurate for single- and double-line outages. Our study suggests that the AC power flow is better at capturing outage patterns and sacrificing some precision could yield substantial improvement in identification accuracy. These findings could contribute to the development of future control schemes that help power systems resist and recover from outage disruptions in real time.