Utilizing Spatiotemporal Data Analytics to Pinpoint Outage Location

TL;DR

This paper presents a novel spatiotemporal data analytics method that integrates outage management, GIS, and vehicle data to accurately identify fault locations during outage post-event analysis.

Contribution

It introduces a new approach combining multiple data sources to improve fault location accuracy in outage management.

Findings

Enhanced fault localization accuracy

Improved post-event analysis insights

Integration of diverse data sources

Abstract

Understanding the exact fault location in the post-event analysis is the key to improving the accuracy of outage management. Unfortunately, the fault location is not generally well documented during the restoration process, creating a big challenge for post-event analysis. By utilizing various data source systems, including outage management system (OMS) data, asset geospatial information system (GIS) data, and vehicle location data, this paper creates a novel method to pinpoint the outage location accurately to create additional insights for distribution operations and performance teams during the post-event analysis.