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

50–80% faster PQ event triage and root-cause identification through automated classification and correlation20–40% fewer truck rolls and 15–30% lower PQ investigation OPEX via targeted dispatch and prioritized mitigation25–50% reduction in PQ complaints and measurable avoidance of C&I downtime/penalty exposure (often $250k–$2M/year per affected site)

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.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0