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Utilities Business Review | Friday, August 14, 2026
A transformer may still be running when a replacement is years away from delivery. For municipal utilities and cooperatives that cannot keep a large inventory of spares, knowing the condition of that transformer affects procurement as much as maintenance. Replacing it too early ties up capital unnecessarily. Missing a developing fault creates the opposite risk: an outage at a substation or generation site with no replacement readily available.
Periodic dissolved gas analysis provides useful information, but a lot can happen between samples. Electrical faults may develop during those intervals, and the chemical signs may not become clear until insulation damage has progressed. Online monitoring can provide more continuous visibility, although the cost may make it impractical to use across an entire mixed fleet. The information can also end up spread across different monitoring systems, leaving engineers to piece together readings when they are trying to judge fault severity or remaining service life.
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Early detection is valuable only if engineers can trust what the system is telling them. An emerging internal fault has to be distinguished from normal background noise without every unusual waveform triggering an alarm. Sensitivity is only part of that equation. Maintenance teams also need to know what kind of problem they may be dealing with, whether it is partial discharge, arcing, a bushing issue or a power-quality disturbance. Frequent false positives consume inspection time and, over time, can make crews less confident in the alerts they receive.
How the equipment is installed can make a significant difference across an aging fleet. Monitoring systems that call for shutdowns, tank modifications, wiring changes or large numbers of sensors are harder to justify when transformers need to remain energized. Non-invasive monitoring avoids some of that disruption, but utilities still need to consider sensor coverage, supported voltage classes, calibration requirements and how an event is localized. Ideally, the same architecture can be deployed first on a troublesome transformer and later expanded across the fleet without having to redesign the monitoring program.
“Its continuous health assessment supports recommended maintenance actions and can incorporate historical dissolved gas records along with other condition data.”
The data also has to lead somewhere. Dynamic health scores, degradation trends, fault localization and estimates of time to failure can inform decisions about when an asset needs inspection and when replacement planning should begin. Electromagnetic signals may also need to be viewed alongside dissolved gas, pressure, temperature or vibration data to build a fuller picture of asset condition. Engineers need access to the technical detail behind those findings, while asset managers need enough clarity to decide whether continued monitoring is appropriate or a closer investigation is warranted.
The electrical environment can affect what the system sees. Transformers operating near inverters, for instance, may produce different noise patterns from standalone substation units. Continuous data collection can improve classification over time, but utilities still need confidence that the model can tell site-specific interference from fault signatures that appear consistently across assets. Physics-based interpretation adds another check on pattern recognition, particularly when one platform is expected to cover both low- and high-voltage equipment.
Magnefy combines high-speed electromagnetic sensors with machine-learning fault classification to provide this visibility without invasive installation. Its continuous health assessment supports recommended maintenance actions and can incorporate historical dissolved gas records along with other condition data. The system distinguishes partial discharge, arcing, corona and harmonic disturbance. Compact hardware, support for multiple sensors, installation without shutdown and coverage across voltage classes give utilities the option to start with selected assets and expand monitoring over time. For utilities managing aging transformers with long replacement lead times, the resulting insight can guide both maintenance timing and decisions about when to begin procurement.
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