Energy Provider: Detect Grid Faults Before Customers Call

A regional utility with 300 employees operates the electricity and gas network for 180,000 households. The control room processes 50,000 sensor data points per day — voltages, loads, flow rates, temperature readings. Faults rarely occur without warning: in the hours before an outage, measurements often show subtle patterns that experienced network engineers recognise — but which get lost in the data volume without systematic monitoring.

Critical Infrastructure Demands Maximum Control

Energy supply networks are critical infrastructure. Data from network control operations may not be transferred to external cloud services — for both security and regulatory reasons. This effectively rules out AI offerings from external providers.

Local Grid Analysis with SoverIQ

SoverIQ Stack runs in the secured control room environment. The model was calibrated on 8 years of historical fault data:

• Real-time anomaly detection: flag deviations from normal patterns in sensor time series
• Fault correlation: are anomalies appearing at multiple measurement points simultaneously? Which network element is the likely origin?
• Forecasting: is a trend continuing? What is the probability of an outage in the next 4 hours?
• Automatic briefing: shift handovers are summarised automatically — ongoing anomalies, maintenance work, open tickets

Results

Proactive fault resolution before customer impact rose from 18% to 61% of all cases. Average outage duration per fault fell by 28%. Customer complaints about unannounced outages dropped by 55%. The investment pays back through a single major fault prevented per year.