AI-Driven Grid Intelligence Over 5G
Distributed sensor networks detect faults, optimise renewable dispatch, and reduce transmission losses in real time — bringing AI inference to the grid edge where decisions matter most.
Talk to an ExpertIntelligence at Every Node of the Grid
Modern energy grids are becoming more complex — distributed renewables, bidirectional power flows, and volatile demand patterns challenge traditional SCADA systems designed for a simpler era. OranSense brings AI inference to the grid edge, processing sensor data from substations, wind farms, solar arrays, and distribution networks in real time.
Private 5G connectivity replaces legacy serial communications across geographically dispersed assets. Edge AI models detect faults in milliseconds, predict demand curves hours ahead, and optimise renewable dispatch dynamically — reducing curtailment and maximising grid stability.
Key Capabilities
Real-Time Fault Detection
AI models analyse voltage, current, and frequency data from grid sensors to detect faults, partial discharges, and insulation degradation in milliseconds — before outages occur.
Renewable Dispatch Optimisation
Predict solar and wind generation 4 hours ahead using weather data and historical patterns. Optimise dispatch schedules to maximise renewable utilisation and minimise curtailment.
Demand Forecasting
ML models trained on consumption patterns, weather, and economic indicators forecast demand at substation level with 98% accuracy. Enable proactive load balancing and reduce reserve margins.
Substation Automation
Edge AI enables autonomous protection relay coordination, automatic fault isolation, and self-healing switching sequences — reducing outage duration from hours to seconds.
Asset Health Monitoring
Continuous monitoring of transformer health, cable temperature, and switchgear condition. Predictive maintenance scheduling extends asset life and prevents catastrophic failures.
Cybersecurity for OT Networks
AI-powered anomaly detection on operational technology networks. Identify unusual command sequences, unauthorised access, and protocol anomalies that indicate cyber intrusion.