AI Cable Fault Localization

Machine learning for detecting and locating faults in power cables

The Problem

“Pinpoint cable faults faster, cut outage duration”

Organizations face these key challenges:

Distance-to-fault estimates are often inaccurate due to cable aging, temperature, sheath bonding, multiple joints, and network topology, causing repeated testing and trial excavations

Outage restoration is delayed by manual data gathering across SCADA, relay records, GIS, and test equipment outputs, with inconsistent interpretation between technicians

High cost and safety risk from unnecessary excavation, traffic control, and work near other buried utilities, plus reputational and regulatory impacts from extended outages

Impact When Solved

20–40% reduction in average time-to-locate and restore for underground cable faults30–50% fewer trial excavations and repeat truck rolls, lowering reinstatement and contractor costsImproved reliability performance (lower SAIDI/SAIFI) and reduced penalty/compensation exposure through faster, more predictable restoration

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0