AI Power Quality Analytics
Detects and classifies harmonics, sags, swells, and transient events from waveform data to pinpoint sources and prevent equipment damage.
The Problem
“Hidden power quality issues drive outages and losses”
Organizations face these key challenges:
PQ event data is high-volume and fragmented across PQ meters, relays, SCADA/DMS/OMS, AMI, and DER platforms, making correlation slow and error-prone
Root-cause attribution and event localization (feeder vs substation vs customer) often requires expert manual analysis and field visits, delaying mitigation
Increasing DER/EV penetration introduces variable harmonics, rapid voltage fluctuations, and switching transients that static thresholds and periodic audits miss
Impact When Solved
The Shift
Human Does
- •Review PQ meter, relay, SCADA, and complaint logs to identify possible disturbance events
- •Correlate feeder, substation, and customer-site data to infer likely source and location
- •Dispatch field investigations and portable measurements after repeated complaints or equipment trips
- •Decide mitigation actions such as filter installation, tap changes, capacitor tuning, or setting updates
Automation
- •Apply fixed threshold alarms and standards-based limit checks to flag PQ violations
- •Generate basic PQ trend charts, event logs, and compliance reports
- •Aggregate periodic readings from available monitoring sources for manual review
Human Does
- •Approve mitigation priorities and operating changes based on ranked PQ findings
- •Review high-impact or ambiguous root-cause cases and decide on field escalation
- •Authorize customer, feeder, or substation interventions and outage-related coordination
AI Handles
- •Continuously monitor waveform and PQ data to detect harmonics, sags, swells, flicker, unbalance, and transients
- •Classify events and estimate likely source location across utility-side and customer-side assets
- •Correlate PQ events with AMI, relay, outage, DER, and weather signals to prioritize root-cause triage
- •Generate explainable alerts, geospatial views, and recommended investigation queues for operators
Operating Intelligence
How AI Power Quality Analytics runs once it is live
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 authorize customer, feeder, or substation interventions without approval from a power quality engineer or grid operations supervisor. [S1][S2]
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 AI Power Quality Analytics implementations:
Real-World Use Cases
AI emergency scenario simulation for nuclear plant response planning
AI runs thousands of nuclear emergency what-if drills on a computer and helps choose the best response before a real problem happens.
AI-assisted grid congestion management
Use AI to help power-grid operators spot and manage overloaded parts of the grid before they become bigger problems.
AI Power Grid Congestion Management
This AI system helps manage electricity grid congestion by optimizing the layout and connections of the grid, reducing costs and emissions.