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:

1

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

2

Root-cause attribution and event localization (feeder vs substation vs customer) often requires expert manual analysis and field visits, delaying mitigation

3

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)

The Shift

Before AI~85% Manual

Human Does

  • Review every case manually
  • Handle requests one by one
  • Make decisions on each item
  • Document and track progress

Automation

  • Basic routing only
With AI~75% Automated

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

Free access to this report