Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
The energy distribution grid is a critical infrastructure challenged with shifting requirements induced by the skyrocketing importance of green energy. Particularly, legacy assets—such as medium-voltage switchgear cabinets and circuit breakers—need to be maintained to prevent energy outages and reduce resource consumption. While related research has abundantly presented algorithms for condition-based maintenance, no design knowledge is available to prescribe how an information system for this purpose ought to be designed. In a design science research project, we develop an information system for condition-based maintenance of legacy assets in the medium voltage distribution grid that utilizes geospatial data. Our design integrates Enterprise Resource Planning (ERP) functionality with Geographic Information Systems (GIS) and a Machine Learning System (MLS) for predicting outages. We demonstrate a current proof-of-concept and conclude by presenting a set of theoretical hypotheses that can guide the evaluation once the system is available.
Utilizing Geographic Information Systems for Condition-Based Maintenance on the Energy Distribution Grid
Online
The energy distribution grid is a critical infrastructure challenged with shifting requirements induced by the skyrocketing importance of green energy. Particularly, legacy assets—such as medium-voltage switchgear cabinets and circuit breakers—need to be maintained to prevent energy outages and reduce resource consumption. While related research has abundantly presented algorithms for condition-based maintenance, no design knowledge is available to prescribe how an information system for this purpose ought to be designed. In a design science research project, we develop an information system for condition-based maintenance of legacy assets in the medium voltage distribution grid that utilizes geospatial data. Our design integrates Enterprise Resource Planning (ERP) functionality with Geographic Information Systems (GIS) and a Machine Learning System (MLS) for predicting outages. We demonstrate a current proof-of-concept and conclude by presenting a set of theoretical hypotheses that can guide the evaluation once the system is available.
https://aisel.aisnet.org/hicss-55/li/gis/2