System SAIDI (Mins)
88.4
MPSC Regulatory Target: < 95.0
System CAIDI (Hours)
2.77
Avg Interruption Duration
Outages Prevented (YTD)
16,420
Automated Reclosers + Smart Grid
Avg Restoration Time
166.1 m
Across 1,200 Monitored Circuits
Critical Vulnerability Alerts
27
Active Staging Triggers
Circuit Stress and Weather Input Parameters
Predictive Inference and Dispatch Guidance Live Reactive
Compound Failure Risk Index
--
Calculating...
| Predicted Restoration Duration | -- |
| 95% Confidence Interval | -- |
| Estimated Customer Hours Lost | -- |
| Active Asset Profile Evaluated | -- |
| Model Algorithm Executed | Ridge Regression + Stratified Ensemble |
Automated Crew Staging Directive:
Updating directive...
Public Safety Protocol:
System calibrated with Recall priority.
Production SQL CTE Engine: Neighborhood Statistical Outliers
Live results executed directly on SQLite database joining outage_events, substation_locations, equipment_assets, weather_readings, and customer_impact. Statistical outliers calculated via Z-score against system baseline. Click any row to load into the simulator.
| Neighborhood | County | Month | Outages | Z-Score | Customers Impacted | Customer Hours Lost | Avg Equipment Age | Classification | Action |
|---|---|---|---|---|---|---|---|---|---|
| Executing SQL multi-stage CTE query... | |||||||||
Azure Data Lake and Databricks Telemetry Monitor
| Population Stability Index (PSI) | 0.042 (Normal Stability) |
| Kolmogorov-Smirnov Statistic | 0.038 (No Drift Detected) |
| Inference Endpoint Latency | 14.2 ms (SLA < 50 ms) |
| ADLS Delta Lake Sync | Synchronized (Sub-second) |
| Active Model Registered | models:/GridPulse_Restoration_Predictor/v3.12 |
Production Model Governance Registry
| Model Name | Telemetry Status | Baseline Performance | Audit Check |
|---|---|---|---|
| Restoration_Duration_v3 | STABLE | MAE: 40.5 min | 5 mins ago |
| High_Risk_Classifier_v2 | OPTIMAL | Recall: 100.0% | 5 mins ago |
| Feeder_Overload_Forecaster_v1 | STABLE | MAE: 18.4 kW | 12 mins ago |
Governance Assurance: All model predictions are logged with input feature snapshots to ADLS for regulatory audit compliance under Michigan Public Service Commission guidelines.
Production Web Scraping & Automated Data Mining Pipeline
Autonomous data extraction infrastructure architected by Dr. Ronald Cornish. Powered by headless browser agents (Playwright, Puppeteer), asynchronous HTTP workers, and NLP entity extraction engines to continuously scrape, normalize, and ingest external telemetry into the electric distribution lakehouse.
| Target Data Source | Scraper Architecture | Frequency | Target Delta Schema | Harvest Rate | Transformation & Extraction Logic | Live Status |
|---|---|---|---|---|---|---|
| Loading automated data mining pipelines... | ||||||
GridPulse Dispatch Operations Copilot
Welcome Dr. Cornish. GridPulse Operations Copilot is connected to ADMS telemetry and our predictive modeling engine. Ask any question regarding circuit risk, weather forecasts, or crew staging directives.