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 every case manually
- •Handle requests one by one
- •Make decisions on each item
- •Document and track progress
Automation
- •Basic routing only
Human Does
- •Review edge cases
- •Final approvals
- •Strategic oversight
AI Handles
- •Automate routine processing
- •Classify and route instantly
- •Analyze at scale
- •Operate 24/7
Technologies
Technologies commonly used in AI Power Quality Analytics implementations:
Real-World Use Cases
AI in Energy Industry: Smart Grid Optimization and Energy Management
This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.
AI Grid Congestion Management
This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.
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.