Views: 0 Author: Site Editor Publish Time: 2026-10-22 Origin: Site
Fuse degradation is fundamentally thermo-electrical. Elevated conductor temperature accelerates interdiffusion within the fuse element, and the maximum element temperature is the decisive criterion for long-term stability. Temperature cycling adds a mechanical stressor: repeated expansion and contraction work-harden the element, while sand displacement in filled fuse tubes can impose additional strain. Moisture ingress and contact oxidation further raise resistance, creating a positive feedback loop between heat generation and degradation. Because fuse performance is temperature-dependent—higher ambient temperature effectively lowers the fuse rating—temperature alone, without current context, cannot distinguish load-driven heating from degradation-driven heating. Current is therefore indispensable as a co-variable.
A practical monitoring layer combines a Rogowski-coil current sensor with a fiber-optic or contact temperature sensor mounted at the fuse body. Reported implementations achieve current accuracy of ±0.5% and temperature accuracy of ±0.1 °C, sufficient to resolve early-stage thermal drift. Sensor nodes may be energy-autonomous, harvesting power capacitively from the conductor, and communicate over private LoRa or 5G links to an edge gateway. The edge gateway performs local filtering and feature extraction, reducing communication burden while preserving the timing information essential for thermal-transient analysis.
Two complementary assessment paths are available. The first is a physics-based expected-temperature model: given the measured current and known ambient conditions, an expected steady-state temperature is computed; sustained positive deviation—the “thermal deviation value”—indicates increased internal resistance or degraded heat dissipation. The second is a population-statistics path: fuses in the same cooling area are grouped by load ratio, and a fuse whose temperature departs significantly from its group median is flagged as an outlier. This avoids reliance on rated parameters when device heterogeneity is high. More advanced formulations normalize the current–temperature relationship through a thermo-electric coupling coefficient and apply thermal-lag compensation, stripping away load-fluctuation interference before trend analysis. The output is not a binary alarm but a degradation trajectory, from which remaining-life or replacement-urgency can be inferred.
Replacement should be triggered by the conjunction of persistent thermal deviation and accumulated thermal stress, not by a single threshold crossing. A fuse that repeatedly operates near its element-temperature limit accumulates interdiffusion damage even without visible external symptoms. Edge-computed alarm messages—containing fuse ID, recent temperature, thermal deviation, feeder circuit, and timestamp—allow maintenance crews to prioritize replacements during planned outages. This converts fuse maintenance from a reactive activity into a scheduled, condition-based intervention, reducing unplanned outages and avoiding the economic cost of premature or missed replacement.
Temperature–current monitoring supplies the two variables needed to separate load effects from degradation effects in transmission-line fuses. By combining a physics-based thermal expectation with population-level outlier detection and trend tracking, utilities can identify aging fuses before they fail and replace them proactively. The sensing hardware is mature; the remaining challenge is embedding the assessment logic into existing asset-management workflows so that condition data translates reliably into maintenance action.
