Case Study: Duke Energy

How Awesense helped Duke Energy gain visibility into their assets at the grid edge and improved their data quality for advanced analytics

Scope Snapshot

Facilitate distribution grid visibility by increasing data quality and combining the various data sources from grid edge smart meter assets and SCADA devices at the base of feeders to allow for advanced analytics.

The Data
  • ~7.2M meters grid GIS
  • Time series data from smart meters, SCADA devices, and distribution line sensors
Awesense Tools Used
  • Awesense Raptor Sensors
  • AI Data Engine
  • True Grid Intelligence (TGI) for asset and grid edge situational awareness.

The Challenge

Duke Energy needed more visibility between SCADA at the substation and smart meters at the grid edge and also needed to increase the degree of accuracy of their GIS data. The utility did not have a solution to bring together the various data sources active in the grid to create and conduct advanced data analysis.

The Solution & Results

Awesense ingested and integrated data from measurement sources active in the grid into the Energy Transition Platform. During the ingestion process, the Awesense AI Data Engine executed its data-driven Validation, Estimation and Error Correction (VEE) algorithms against all geospatial, connectivity and time series data entering the system. The AI Data Engine discovered over 500,000 quality issues. Following multiple iterations, 97% were corrected in a matter of weeks. The Engine synchronized all time series data and associated time series measurements with their geospatial representation in the network model. The result is in an Energy Data Model (EDM), from which Awesense applications could easily access all data and other Duke internal systems requiring access to critical data. Utilizing the EDM constructed by the Data Engine, various applications provided by Awesense performed grid-wide geospatial analysis. Awesense provided Duke Energy with a situational awareness engine capable of displaying real-time data anywhere in the distribution grid from various sources. Historical data was used to understand trends and issues occurring over time in the network, and data was continuously ingested to maintain an understanding of grid performance.

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