Power Quality Analytics
Detects and classifies harmonics, sags, swells, and transient events from waveform data to pinpoint sources and prevent equipment damage.
The Problem
“Detect and explain power quality disturbances from waveform data before they damage equipment or violate compliance limits”
Organizations face these key challenges:
Static thresholds do not generalize across feeders, substations, and industrial sites
Manual waveform review is slow and requires scarce power quality specialists
High-frequency waveform data volumes are difficult to process in real time
Disturbance signatures overlap and can be hard to classify with simple rules
Renewable intermittency changes baseline operating conditions and event patterns
SCADA, meter, and waveform data are often siloed and poorly synchronized
Critical infrastructure environments require secure, auditable integration
Edge devices have tight latency, memory, and power constraints
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 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 feeder, substation, customer, or outage-related interventions without operator judgment. [S8][S9]
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 Power Quality Analytics implementations:
Key Players
Companies actively working on Power Quality Analytics solutions:
Real-World Use Cases
Embedded smart-grid controller workflow for visualized PQ event monitoring in renewable-integrated microgrids
A controller in a small smart grid collects voltage and current signals, turns them into pictures and measurements, and shows operators what kind of power problem is happening so they can keep electricity stable.
Threshold-breach forecasting for harmonic distortion compliance
The system watches harmonic distortion levels and warns when they are likely to cross accepted limits, helping the facility stay within power-quality rules.
Adaptive threshold learning for power quality disturbance setpoints
Instead of forcing engineers to guess alarm limits, the meter can learn good threshold settings for events like voltage dips and spikes.
Power quality disturbance classification using deep wavelet-convolution-transformer models
An AI system looks at electrical waveform signals, breaks them into useful frequency patterns, and labels problems like sags, swells, or harmonics automatically.
Resource-aware deployment of PQD classifiers for constrained monitoring environments
Different AI models can be chosen depending on whether you care more about speed and small size or the best possible accuracy when monitoring power disturbances.