Grid Reliability Risk Insight
AI-powered fault detection and diagnosis for energy distribution operations, identifying illegal dumping, sensor tampering, equipment faults, and operational fraud early to protect service quality and reduce costs.
The Problem
“Detect illegal dumping, sensor tampering, equipment faults, and operational fraud early in distribution operations”
Organizations face these key challenges:
Telemetry is fragmented across SCADA, AMI, IoT sensors, GIS, and maintenance systems
Static thresholds fail to capture context-dependent abnormal behavior
Manual reviews are slow, inconsistent, and difficult to scale
Field inspections are expensive and often happen after damage is done
Impact When Solved
The Shift
Human Does
- •Review threshold alarms, audit findings, and exception spreadsheets for suspicious activity
- •Compare telemetry, maintenance records, and field reports to identify likely faults or tampering
- •Dispatch inspections or maintenance crews after abnormal events are noticed
- •Investigate fraud, illegal dumping, or irregular operations through manual case review
Automation
- •Generate basic threshold alarms from operational and sensor data
- •Store fragmented telemetry and inspection records for later review
- •Surface simple exception reports based on predefined rules
Human Does
- •Approve response actions for high-risk anomalies and suspected fraud or tampering cases
- •Review AI-prioritized incidents and decide on inspections, maintenance, or investigations
- •Handle ambiguous exceptions, safety-critical events, and compliance-sensitive escalations
AI Handles
- •Continuously monitor telemetry, geospatial activity, work history, and field signals for anomalies
- •Detect and rank suspicious events such as illegal dumping, sensor tampering, equipment faults, and operational fraud
- •Correlate evidence across assets, locations, and historical patterns to suggest likely root causes
- •Route prioritized incidents, recommend next actions, and draft investigation summaries
Operating Intelligence
How it works
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
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
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not approve high-risk response actions or suspected fraud or tampering cases without human review and judgment [S1].
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Grid Reliability Risk Insight implementations:
Key Players
Companies actively working on Grid Reliability Risk Insight solutions: