Electric Utilities

Load variation and
aging infrastructure
are producing a
problem utilities
cannot see.

Transformer fleets sized for stable baseload generation are now absorbing continuous load variation from intermittent renewables, distributed generation, and a changing source mix. Every swing generates mechanical stress on windings and insulation that were already aging under decades of continuous service.

The combination of geriatric assets and volatile loading conditions is extraordinary. Conventional inspection programs were not built for it.

VIE provides continuous transformer intelligence that detects this accumulated degradation months before failure, at fleet scale, without de-energizing a single asset.

The Industry’s Biggest Risk Is Not Asset Failure.
It's Decision Latency.

For a VP of Asset Management, the aging transformer fleet carries three distinct exposure layers: regulatory compliance risk under NFPA 70B-2023 and NERC reliability standards, unplanned outage liability with direct financial and community impact, and a mounting deferred capital spend problem that schedule-based maintenance can no longer contain.

Grid failure is not an abstraction. The 2021 ERCOT winter crisis demonstrated what happens when aging infrastructure meets conditions it was not prepared for. Assets that appeared operational failed in sequence. The regulatory and human consequences were severe.

NERC reliability standards carry financial penalties for failures in critical transmission assets. State regulators now track outage duration and frequency with greater scrutiny than they did a decade ago. NFPA 70B-2023 is now in effect, mandating condition-based maintenance documentation across electrical infrastructure. Time-based inspection schedules cannot generate the continuous records these frameworks require.

The monitoring gap compounds the problem. Periodic oil tests, annual inspections, and scheduled power factor testing all share the same flaw: they sample a transformer at a fixed moment and leave the asset unmonitored for weeks or months afterward. Transformer degradation does not observe that schedule. Winding looseness, insulation loss, partial discharge, and overheating oil develop continuously. By the time a scheduled test catches them, the window to source parts and schedule a repair is often already closing.

Workforce attrition is narrowing that window further. The engineers who know how to interpret these signals are leaving the industry. Utilities dependent on human expertise for transformer health decisions carry a risk that compounds every year those positions go unfilled.

VIE closes the gap. Continuous, autonomous transformer intelligence that does not wait for a scheduled test or a senior engineer's interpretation. Detection happens months ahead of failure. The compliance record builds automatically.

3 to 6 months.

That is the typical lead time between
a VIE detection and a failure event.

The Transition the Industry Is Already Making

Every major electric utility is moving from reactive asset ownership to continuously informed infrastructure management. NFPA 70B-2023 mandated that transition in practice. The question is no longer whether to make it. The question is how fast.

From

Schedule-Based Monitoring

  • Threshold alarms that trigger only when failure has already begun
  • Periodic testing with long blind intervals between samples
  • Single-asset, siloed data with no fleet-level context
  • Reactive maintenance cycles driven by failure events

To

Continuous Condition Intelligence

  • Early failure mode identification weeks to months before the failure event
  • 24/7 visibility with no blind spots between inspection cycles
  • Fleet-wide risk data across every asset, updated continuously
  • Predictive capital planning driven by real-time risk rankings

Every major electric utility is moving from reactive asset ownership to continuously informed infrastructure management. NFPA 70B-2023 mandated that transition in practice. The question is no longer whether to make it. The question is how fast.

VIE is the platform that makes the
transition operational, not theoretical.

What VIE Delivers for Electric Utilities

VIE converts transformer fleet management from a reactive, episodic process into a continuous operating system. The platform builds a virtual model from each transformer's geometry and refines it with every sample — detecting deviation from expected behavior months before failure, without waiting for a scheduled inspection or a senior engineer's review.

Enables real-time fleet-level risk visibility.

Every transformer in your fleet, visible in one platform, continuously updated. Risk rankings surface the assets that need attention and let leadership make informed capital and maintenance decisions from one view.

Extends asset life and defers capital spend.

Accurate condition data lets assets run longer on evidence, not schedules. Replacement decisions are driven by verified health data, not calendar milestones. Deferred capital spend compounds across a large fleet.

Reduces operating costs.

Early detection converts emergency responses into planned maintenance. Planned maintenance costs a fraction of unplanned failure in labor, logistics, and parts.

Aligns field execution with fleet risk.

Real-time risk rankings translate directly into field team work orders. The same data that drives executive decisions drives technician dispatch — no priority gap, no manual translation required.

Decreases truck rolls and maintenance overhead.

Condition-based dispatch replaces time-based inspection schedules. Field teams respond to confirmed conditions, not routine check-ins. Fuel, labor, and scheduling overhead decrease across the fleet.

Supports extreme weather preparedness and inventory planning.

Transformer fleet risk is not static when the grid comes under extreme weather stress. VIE's continuous fleet intelligence tells utilities which assets are most vulnerable during surge conditions and informs how much spare transformer inventory to hold — reducing stockpile costs during steady-state operations and emergency lead time exposure when conditions deteriorate.
Proven at Scale

GETCO Deployment

VIE's models do not run on simulations. They run on real transformer data from real deployments.

VIE Technologies deployed the MyVIE continuous monitoring platform on 50 oil-filled transformers under a 3-year service agreement (December 2022). AI-powered, non-invasive sensors required no access to internal components and no disruption to operations.

Within six weeks of deployment, VIE identified four at-risk assets that conventional inspection had not flagged.

Issues detected:

  • Higher vibration levels, high mid-frequency vibration. Flagged for potential core looseness. Recommended for immediate electrical analysis.
  • Elevated vibration, high surface temperature, significant nighttime temperature variation across sensors. Flagged for potential oil breakdown or insulation loss
  • High vibration frequency distortion. Flagged for potential deformed winding.
  • High vibration frequency distortion, elevated temperature. Placed on watchlist.

MyVIE didn't predict a vague risk. It identified four specific transformers, flagged specific failure modes with confidence ratings, and enabled GETCO's team to validate each finding with targeted tests—then act. Three units confirmed at immediate-shutdown status. One replaced proactively. Backup generation costs, regulatory exposure, and cascade failure risk all avoided.

Source: GETCO

Leading Indicators.
Months to Plan.

The cost of lagging indicators is not abstract for electric utilities. Transformer replacement timelines stretch three to five years under normal conditions. Under emergency conditions, that timeline extends further. An unplanned failure that could have been a scheduled replacement costs many times more in labor, logistics, expedited parts sourcing, and outage consequences.

For aging fleets under load variation, that window compresses exactly when it matters most. The grid comes under surge precisely when the least-monitored assets are at highest risk.

Every month of advance warning has measurable financial value. VIE provides months.

The platform detects failure mode signatures as they develop, when there is still time to plan, source parts, staff a crew, and schedule the work on your terms.

VIE detects electrical, mechanical, and thermal failure modes 3 to 6 months before the failure event. No scheduled outages. No blind intervals. No reactive surprises.

NFPA 70B-2023 Compliance, Built In

NFPA 70B-2023 mandates risk-based, condition-based maintenance with documented results. VIE addresses six mandatory clauses directly — including documentation, prioritization, monitoring technology, and remote access requirements.

The myVIE platform automatically logs every transformer health data point, alert, and maintenance event with timestamps and complete audit trails. Reports export on demand in formats ready for compliance review.

VIE is field-deployable without downtime. Documentation is active from the moment sensors go live. Compliance is not a project you complete after deployment. It starts on day one.

By the Numbers

800+

Transformers monitored globally, from 500 kVA dry-types to 500+ MVA

2+ GW

Of global generation capacity covered

3–10x

ROI delivered in months (KPMG-validated)

Under 30 Min

Installation per transformer, no de-energization

Frequently Asked Questions

Does VIE work on the transformer types in our fleet?

Yes. VIE sensors install on the external surface of any transformer — wet or dry, pad-mounted or substation, any make, model, voltage class, or vintage. The transformer stays fully energized throughout the installation process. No modifications to transformer internals. No de-energization required. Sensor count scales with transformer size: 3 to 4 sensors on units under 10 MVA, 5 to 7 on units between 10 and 100 MVA, and 6 to 9 on units above 100 MVA.

Transformers don't fail frequently. Why do I need this?

Transformer failures are low-frequency events — and that frequency is exactly the problem. Because failures are rare, they are easy to defer planning for, easy to underestimate, and easy to assume will not apply to your fleet until they do. The consequences of a single failure at a substation serving critical load are not proportional to the statistical frequency. A 500 MVA transformer on a transmission corridor serving hundreds of thousands of customers has a replacement lead time of three to five years. The operational and regulatory exposure from that single failure does not fit into any maintenance budget.

Frequency also does not account for the slow-developing degradation that produces sudden failures. The transformer that fails without warning was typically degrading for months before failure. VIE detects that degradation during the months when intervention is still possible.

How does VIE establish a baseline? Why doesn't it learn from the asset's own history?

Most condition-monitoring methods learn a baseline from the asset's own history and then flag departures from it. That approach assumes the asset is healthy when monitoring begins. VIE does not, because operators usually install the sensors on transformers of unknown condition. A baseline learned from a unit that already carries a defect would absorb the defect as normal and hide the very problem the system exists to find.

VIE uses a different reference. From the approximate geometry of the specific transformer, VIE builds a virtual model of the unit, grounded in how a transformer of that construction should vibrate and conduct heat. VIE compares each measurement to this model rather than to the asset's own past. That lets the platform identify a defect present at the time of installation, not only one that develops later.

VIE refines the virtual model with every sample. A typical sensor records 3 to 4 times per hour, so the model improves continuously and converges on an accurate, unit-specific reference. It never treats an existing fault as the normal state. The same geometry also informs where to place the sensors, so each one sits where it best resolves the structure-borne and fluid-borne signatures. Each sensor carries more than ten years of battery life, which sustains this sampling rate for the service life of the installation without intervention.

VIE does not learn its baseline from operating history. A system that does can absorb a slowly developing fault and treat it as the new normal. A physics-based model cannot. A clean reading from VIE is an affirmative statement that the measured mechanical and thermal behavior matches a healthy unit — not merely the absence of an alarm.

How does VIE compare to online dissolved gas analysis (DGA)?

Online DGA requires a physical connection to the transformer's oil system, penetrating oil containment to place a sensor in the oil. VIE sensors are external and non-invasive. No oil system penetration. No new access point to maintain.

Online DGA is threshold-based: an asset within its gas concentration thresholds generates no alert, even if its insulation system is degrading in a way that will cause failure during the next peak load event or extreme weather condition. The most dangerous failures are often the ones that look fine on gas readings right up until the moment they fail. VIE models what each transformer should produce based on its geometry, load, and ambient conditions. A deviation from that expected behavior generates a finding regardless of gas levels.

Online DGA covers one failure mode channel on the transformers it is installed on. VIE covers five failure mode channels across every transformer in the fleet simultaneously. And at $8,000 to $30,000 per unit installed plus $500 to $1,500 per year in maintenance, fleet-wide online DGA is cost-prohibitive for most utilities. In practice, utilities deploy online DGA on their highest-risk assets and leave the rest unmonitored. That is fleet-level blindness — documented and accepted.

VIE enables fleet-wide coverage at a cost that scales. Lab DGA results feed back into VIE's platform, where the AI combines gas data with continuous vibration, thermal, and weather analysis to extract insights no domain expert could derive from gas data alone. The accuracy compounds with every additional lab result. Every lab DGA sample targeted by VIE is more valuable than one pulled on a fixed schedule.

Does VIE satisfy NFPA 70B-2023 documentation requirements?

Yes. NFPA 70B-2023 mandates condition-based maintenance with documented results. The myVIE platform automatically logs all transformer health data with timestamps and complete audit trails. Reports export on demand in formats suitable for compliance review and inspector documentation requirements.

Does installation require taking transformers offline?

No. VIE sensors mount to the external surface of the transformer tank using two-part epoxy. No de-energization. No modification to internals. No service interruption. Installation takes under 30 minutes per transformer on most units.

Your Fleet. Fully Visible.
Right Now.

Aging transformers. Load variation. A shrinking expert workforce. A mandated compliance standard. The conditions driving electric utilities toward continuous transformer intelligence are not going away.

VIE is deployed today across 800+ transformers globally, from 500 kVA dry-types to 500+ MVA. One sensor failure and zero gateway failures since launch.