AI Outage Management System
The Problem
“Slow, inaccurate outage restoration and communication”
Organizations face these key challenges:
Delayed and fragmented outage signals (customer calls, AMI, SCADA) causing slow fault localization and misclassification of outage scope
Inefficient crew dispatch and switching decisions leading to unnecessary truck rolls, longer restoration times, and higher safety risk
Inaccurate ETAs and inconsistent customer/regulatory communications, increasing call center volume, complaints, and compliance exposure
Impact When Solved
The Shift
Human Does
- •Review SCADA alarms, AMI last-gasp signals, customer calls, and field reports to confirm outage events
- •Infer likely fault location and outage scope from feeder maps, switching orders, and prior experience
- •Assign crews and approve switching and restoration steps based on safety and operational priorities
- •Manually update ETAs, customer communications, and regulatory event records as conditions change
Automation
- •Aggregate incoming outage signals from operational sources
- •Apply rule-based outage grouping and standard restoration estimates
- •Display outage locations, feeder status, and crew information for dispatcher review
- •Produce basic event logs and post-event reporting extracts
Human Does
- •Approve restoration priorities, switching actions, and crew deployment recommendations
- •Handle safety-critical exceptions, conflicting field conditions, and high-uncertainty outage cases
- •Validate communications for major events, critical customers, and regulatory-sensitive situations
AI Handles
- •Fuse real-time grid, meter, weather, DER, and field data to detect outages and estimate fault location and cause
- •Prioritize incidents and generate optimized crew dispatch and restoration sequencing recommendations
- •Monitor event progression continuously and update ETAs, outage scope, and restoration plans as new data arrives
- •Generate targeted customer updates, event summaries, and regulatory reporting narratives with consistent timestamps
Operating Intelligence
How AI Outage Management System runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not execute switching actions or restoration steps without approval from the outage supervisor or grid operations controller. [S1][S2][S3]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
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