POWER SYSTEM TRANSMISSION LINE OUTAGE DETECTION AND IDENTIFICATION: A PHYSICS-INFORMED DATA-DRIVEN APPROACH

Abstract

Tackling the challenges of processing streaming and limited PMU data, this thesis proposes three methods for real-time power system line outage detection and identification. (1) Using voltage phase angle data collected from PMUs, a real-time dynamic outage detection scheme based on alternating current (AC) power flow model and statistical change detection theory is proposed. The method can capture system dynamics since it retains the time-variant and nonlinear nature of the power system. (2) As an extension, a unified detection framework that utilizes both generator dynamic states and voltage information is proposed. The inclusion of generator dynamics makes detection faster and more robust. (3) Lastly, a new way of identifying multiple-line outages underdetermined sparse regression is formulated for better outage localization. The findings of this thesis could contribute to the development of future control schemes that help power systems resist and recover from outage disruptions faster.