An ontology‐based system for discovering landslide‐induced emergencies in electrical grid. Issue 3 (4th March 2020)

• Record Type: Journal Article
• Authors: Phengsuwan, Jedsada; Shah, Tejal; Sun, Rui; James, Philip; Thakker, Dhavalkumar; Ranjan, Rajiv
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-04
• Subjects: data sources discovery -- early warning system -- electrical grid system -- energy management -- high variety data -- IoT data -- landslide hazard -- ontology; Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3899
• Languages: English
• ISSNs: 2161-5748

Abstract

Summary: 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.