ACHIEVING ON-DEMAND RISK MANAGEMENTACROSS GRID OPERATIONS WITH COGNITE DATA FUSION
Identify Areas of High Risk • Prioritize Asset & Line Maintenance • Reduce Wildfire Liability
Rapid change from harsh weather patterns issensors, drones, imaging, Lidar, and other technolo-outpacing traditional methods of scheduled line

gies to collect more data than ever before. But thisinspection and asset management, exposing newdata collection at scale results in a complex digitaland increasing risks across the grid from substation

solution stack that struggles to return the insightsto customer. Given the sheer scale of activity acrossneeded in a time sensitive, actionable way, resulting inthousands of miles of power lines and distributed

compounding data and digital complexity.

substations, the industry is quickly adopting IoTTransitioning to On-Demand Risk Management is a Big Data Fusion Problem
COMMISSION ASSET
MAINTENANCE

ORDER SPARE PARTS

ADJUST LINECAPACITYCOMMISSION WORKORDER

COMMISSIONPATROLS

Capacity
Available(DLR)

Missing
Insulator

HigherDemandExpectedTransformer
Maintenance
Recommended

DEENERGIZE/REENERGIZE

Corrosion
Detected

ElevatedFireRisk

VegetationEncroachment

On-Demand Risk Management — where risks arehelps prioritize longer term capital expenditures into

identified, visualized, prioritized, and actioned in nearupgrades and planning. However, the sheer volumereal-time — is achievable when data aggregation andof data types, sources, analytics, and integrations

analysis are automated, timely, and affordable and caninvolved make this a challenging problem that must

be fluidly integrated into decision-making and work

be broken down into approachable categories oforders. This results immediately in increased visibilityapplications.

into the aggregated risk profile of grid operations andDevelop Your Next-Generation Response to Risk and Retain Control
of Your Data and Analytics
Cognite Data Fusion software offers an industrial data foundation for
rapid development and deployment of risk-based digital applications built
around industry-leading data contextualization and operationalization.
■ Aggregates and contextualizes big data from all available IT and OT
data sources■ Ensures data quality and lineage throughout the dev pipeline and intothe end application■ Empowers data scientists, engineering, domain experts, and analystworkflows■ Enables operationalization and scale of digital applications with openintegrations (APIs/SDKs)

SUBSTATION MAINTENANCE

OVERHEAD INSPECTIONVEGETATION MANAGEMENTWORK ORDER ACTIVITYWORK ORDER ACTIVITYWORK ORDER ACTIVITYDECISION-MAKING & REPORTING
TAKING IN WEATHER, WIND, POPULATION DENSITY DATA INTO ACCOUNTANALYSISREMAINING USEFUL LIFEDUVAL TRIANGLEANALYSISCOMPUTER VISION
SME ANNOTATIONANALYSISSIMULATIONRANGE ANALYSISDATA
TIME SERIESMAINTENANCE HISTORYDATAGIS DATADRONE & HELO IMAGESDATA
LIDAR POINT CLOUDS
POLE TAGSContributing Use Case 1: Transformers

Contributing Use Case 2: Inspections

A Major Norwegian Grid Operator is leveraging

A Major US Utility is evaluating CDF for mass

CDF to monitor the conditions of their distributed

inspection of Lidar and imagery data for equipment

transformer network.

and vegetation anomalies.

CDF is enabling their domain experts to proactively

By combining these data with GIS and pole tags, theidentify risks of failure so that maintenance can beutility will be able to assign work orders with pinpoint

scheduled accordingly.

precision.

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