The Challenge
This electric utility served 2 million customers across a service territory spanning 15,000 square miles. Their aging SCADA system provided basic grid monitoring but lacked analytical capabilities. Outage response was reactive — crews dispatched after customers called to report power loss. Equipment failures accounted for 60% of unplanned outages, but there was no predictive capability to identify at-risk assets. The utility faced increasing regulatory scrutiny on reliability metrics (SAIDI, SAIFI, CAIDI) and needed to demonstrate improvement to the public utilities commission.
Our Solution
Deployed smart grid sensors and integrated existing SCADA data feeds into Azure IoT Hub, streaming 500,000+ data points per minute from substations, transformers, feeder lines, and smart meters into Fabric Real-Time Intelligence.
Built KQL-based anomaly detection rules that identify abnormal voltage patterns, transformer overloading, phase imbalances, and vegetation contact signatures. Alerts route to the operations center dashboard and mobile devices for field crews.
Developed predictive maintenance models that analyze historical equipment failure patterns combined with real-time sensor data, weather forecasts, and load projections to predict asset failures 1-2 weeks in advance.
Created an operations center dashboard in Power BI with real-time grid topology visualization, outage heat maps, crew dispatch tracking, and reliability metric monitoring (SAIDI/SAIFI/CAIDI) against regulatory targets.
Implemented storm preparation analytics that combine weather forecast data with asset vulnerability models to pre-position crews and materials before severe weather events, reducing storm response times by 45%.
Results
Predictive maintenance and vegetation management reduced unplanned outages by 30% year-over-year.
500,000+ data points per minute from smart grid sensors provide real-time operational awareness.
Pre-positioning crews and materials based on weather predictions cut storm restoration time nearly in half.
Improved reliability metrics directly benefiting 2 million residential and commercial customers.
Reduced equipment replacement costs through condition-based maintenance instead of time-based schedules.
System Average Interruption Duration Index improved 28%, exceeding regulatory commission targets.
Implementation Methodology
Phase 1 (Weeks 1-4): IoT infrastructure. Deployed smart grid sensors, integrated SCADA feeds, configured Azure IoT Hub, and established real-time data pipelines into Fabric.
Phase 2 (Weeks 5-9): Real-time analytics. Built KQL anomaly detection rules, operations center dashboard, outage management visualization, and crew dispatch tracking.
Phase 3 (Weeks 10-14): Predictive models. Developed equipment failure prediction models, vegetation management analytics, and storm preparation decision support tools.
Phase 4 (Weeks 15-18): Deployment and optimization. Rolled out to operations center, trained dispatchers and field crews, tuned alert thresholds, and established monitoring protocols.