Modern power systems characterized by complex topologies require accurate situational
awareness to maintain an adequate level of reliability. Since they are large and spread over …

Online power system event identification and classification are crucial to enhancing the
reliability of transmission systems. In this paper, we develop a deep neural network (DNN) …

The inertia information of both synchronous generators (SGs)/equivalent SGs and converter
interfaced generators (CIGs) are crucial to maintain the frequency stability of modern power …

This article studies robust event classification using imperfect real-world phasor
measurement unit (PMU) data. By analyzing the real-world PMU data, we find that it is …

Time Synchronization Attacks (TSAs) against Phasor Measurement Units (PMUs) constitute
a major threat to modern smart grid applications. By compromising the time reference of a …

Accurate and speedy detection of power system events is critical to enhancing the reliability
and resiliency of power systems. Although supervised deep learning algorithms show great …

This work establishes and validates a Grid Graph Signal Processing (G-GSP) framework for
estimating the state vector of a radial distribution feeder. One of the key insights from GSP is …

We establish a framework for signal processing on product spaces of simplicial and cellular
complexes. For simplicity, we focus on the product of two complexes representing time and …

This paper proposes a novel online measurement-based Wide-Area Voltage Control
(WAVC) method using Phasor Measurement Unit (PMU) data in power systems with Flexible …

This paper develops an accurate and computationally efficient data-driven framework to
detect voltage events from PMU data streams. It develops an innovative Proximal Bilateral …