Distribution Engineer
Voltage regulation and power quality
What You Do Today
Investigate voltage complaints, power quality issues (harmonics, flicker, sags), and design solutions — capacitor banks, voltage regulators, line reconfiguration, or customer-side mitigation.
AI That Applies
AI analyzes AMI voltage data across thousands of meters to identify systemic voltage issues before customers complain, and correlates power quality events with DER operations.
Technologies
How It Works
The system ingests AMI voltage data across thousands of meters to identify systemic voltage issues as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Reactive complaint investigation shifts to proactive identification using AMI data analytics across the entire system.
What Stays
Root cause investigation for complex power quality issues, designing cost-effective solutions, and customer communication about what's causing their problems.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for voltage regulation and power quality, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long voltage regulation and power quality takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“Which compliance checks are we doing manually that could be continuous and automated?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“How would our regulator react to AI-assisted compliance monitoring — have we asked?”
They understand the workflow dependencies that AI tools need to respect
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.