PMU Tracker: A Visualization Platform for Epicentric Event Propagation Analysis in the Power Grid

by   Anjana Arunkumar, et al.

The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as 'smart' systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, and time-synchronized measurements of voltage and current, enabling real-time wide-area monitoring and control. However, the potential benefits of PMUs, for analyzing grid events like abnormal power oscillations and load fluctuations, are hindered by the fact that these sensors produce large, concurrent volumes of noisy data. In this paper, we describe working with power grid engineers to investigate how this problem can be addressed from a visual analytics perspective. As a result, we have developed PMU Tracker, an event localization tool that supports power grid operators in visually analyzing and identifying power grid events and tracking their propagation through the power grid's network. As a part of the PMU Tracker interface, we develop a novel visualization technique which we term an epicentric cluster dendrogram, which allows operators to analyze the effects of an event as it propagates outwards from a source location. We robustly validate PMU Tracker with: (1) a usage scenario demonstrating how PMU Tracker can be used to analyze anomalous grid events, and (2) case studies with power grid operators using a real-world interconnection dataset. Our results indicate that PMU Tracker effectively supports the analysis of power grid events; we also demonstrate and discuss how PMU Tracker's visual analytics approach can be generalized to other domains composed of time-varying networks with epicentric event characteristics.


page 5

page 6


LEAP nets for power grid perturbations

We propose a novel neural network embedding approach to model power tran...

Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification Within a Power Transmission System

As power quality becomes a higher priority in the electric utility indus...

Unsupervised clustering of disturbances in power systems via deep convolutional autoencoders

Power quality (PQ) events are recorded by PQ meters whenever anomalous e...

Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks

Large-scale load-altering attacks (LAAs) are known to severely disrupt p...

Event-Triggered Islanding in Inverter-Based Grids

The decentralization of modern power systems challenges the hierarchical...

Power System Event Identification based on Deep Neural Network with Information Loading

Online power system event identification and classification is crucial t...

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Power grid operation subject to an extreme event requires decision-makin...

Please sign up or login with your details

Forgot password? Click here to reset