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Lookup NU author(s): Top Phengsuwan, Dr Tejal Shah, Professor Philip James, Professor Raj Ranjan
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 John Wiley & Sons, Ltd.Early warning systems (EWS) for electrical grid infrastructure have played a significant role in the efficient management of electricity supply in natural hazard prone areas. Modern EWS rely on scientific methods to analyze a variety of Earth Observation and ancillary data provided by multiple and heterogeneous data sources for the monitoring of electrical grid infrastructure. Furthermore, through cooperation, EWS for natural hazards contribute to monitoring by reporting hazard events that are associated with a particular electrical grid network. Additionally, sophisticated domain knowledge of natural hazards and electrical grid is also required to enable dynamic and timely decision-making about the management of electrical grid infrastructure in serious hazards. In this paper, we propose a data integration and analytics system that enables an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision-making for electrical grid infrastructure management. We prototype the system using landslides as an example natural hazard for the grid infrastructure monitoring. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. Using the knowledge modeled, the prototype system can report the occurrence of landslides and suggest potential data sources for the electrical grid network monitoring.
Author(s): Phengsuwan J, Shah T, Sun R, James P, Thakker D, Ranjan R
Publication type: Article
Publication status: Published
Journal: Transactions on Emerging Telecommunications Technologies
Year: 2022
Volume: 33
Issue: 3
Print publication date: 01/03/2022
Online publication date: 04/03/2020
Acceptance date: 18/12/2019
Date deposited: 26/10/2020
ISSN (print): 2161-5748
ISSN (electronic): 2161-3915
Publisher: Wiley Blackwell
URL: https://doi.org/10.1002/ett.3899
DOI: 10.1002/ett.3899
Notes: Special Issue: Enabling Technologies for Future Mobile and Edge Networks and Enabling AI Technologies for Internet of Energy
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