Keep Your Transformers Reliable

Prevent Costly Outages and Delays with Advanced Transformer Monitoring

The Challenge

As the power grid evolves, NREL research shows transformers face increasing stress from load variability, thermal cycling, harmonics, and reverse power flow—accelerating aging and failure risk. With replacement lead times now measured in years, a single transformer outage can severely disrupt operations and grid reliability. As some of the most valuable and mission-critical assets in power generation and distribution, transformer failures carry significant operational and financial consequences.

Transformers Are Essential in:

Power Generation Plants: step-up and auxiliary transformers
Oil and Gas, Pulp & Paper, and Chemical plants with on-site power generation
Transmission and Distribution Networks: substations and grid interconnects
Renewable Energy: solar and wind farms connecting to the grid

Monitoring Strategies

Transformer health requires a multiphysics approach that captures both internal degradation and external stressors. Early detection of insulation breakdown, winding faults, and cooling issues is key to extending life and avoiding catastrophic events.

Key Strategies Include:

Electromagnetic Interference (EMI) Monitoring for early detection of insulation breakdown
Infrared (IR) Thermography to identify hot spots, cooling inefficiencies, and overload conditions
Electrical Signature Analysis to spot winding faults, load imbalances, and overexcitation
Oil and Moisture Analysis to detect contamination and insulation risk
Dissolved Gas Analysis (DGA) to detect insulation degradation, arcing, and incipient faults

Protect Your Transformers. Protect Your Power.

Discover how Cutsforth helps you safeguard transformer health and grid reliability.

Cutsforth’s Integrated Diagnostic Models provide a digital blueprint for reliability by mapping known failure modes to the sensors and signal features needed to detect them. Built with input from experienced reliability engineers, the models capture optimal sensor placement, failure criticality and likelihood, and diagnostic effectiveness. The result is actionable fault intelligence—combining multiphysics measurements, deep expertise, and AI-driven optimization to clearly show how assets behave and where failures are most likely to occur.

Transformer Major Components and Failure Modes

Click the image hotspots below to see how compressor main components are broken into sub-components with a sample list of associated failure modes for each. Refer to the linked documents below for more details about the Compressor Integrated Diagnostic Model.

Download the Cutsforth Transformer Model Data Sheet for more on transformer failure modes, symptoms, sensors, and criticality.

To learn more about Integrated Diagnostic Models in general, download our Cutsforth Integrated Diagnostic Models White Paper.

The Risks of Doing Nothing

Ignoring transformer health puts the grid and plant operations at serious risk:

Catastrophic Failures with explosive potential and high replacement costs
Unplanned Outages leading to lost generation or service interruptions
Extended Downtime due to long replacement lead times
Safety and Environmental Hazards from oil leaks, fires, or explosions
Premature Aging that shortens service life and increases capital expenditures

How Cutsforth Helps

Cutsforth offers a comprehensive transformer monitoring solution designed for high-value, high-risk assets:

Unified Software Platform: InsightCM™ integrates measurements, waveforms, and alarms in one pane of glass
Measurement Portfolio: monitoring systems across the spectrum of vibration, electrical, thermography, EMI, and more
Actionable Insights: AI-powered analytics combined with expert diagnostics to translate raw data into clear recommendations
Reliability Services: experienced consultants help assess current practices, design monitoring programs, and implement

With Cutsforth, operators gain the early warning system needed to extend transformer life, prevent catastrophic failures, and protect system reliability.