An AI reliability playbook for VPs of Operations responsible for grid signal anomaly detection.
Alerts the wrong things, or worst, doesn't alert at all.
Anomaly detection on the grid is an unusually hard machine learning problem.
Signal quality is the underrated half of the problem.
Alert calibration is the other half.
We spend the first 25 percent of every anomaly detection program on the data pipeline. Sensor health, missing data handling, time alignment across systems, label quality.
We make the trade-off explicit. The operations team chooses where on the precision-recall curve they want to live.
Operators tag every alert as true positive, false positive, or 'don't know yet.' The tagged data is the most valuable training signal in the system. We make tagging easy — one click in the existing operator console.
We spend the first 25 percent of every anomaly detection program on the data pipeline.
We make the trade-off explicit.
Operators tag every alert as true positive, false positive, or 'don't know yet.' The tagged data is the most valuable training signal in the system.
If your anomaly detection alerter is louder than it is useful, the path forward is signal quality, alert calibration, operator-in-the-loop tuning, and continuous evaluation.
Because operator trust scales inversely with noise. A high-volume alerter loses trust no matter how good its catch rate is. We optimize for usable alerts, not maximum alerts.
The audit pack includes the full evidence chain: model card, data lineage, alert log, operator response, post-event reconciliation. We have produced these for FERC, NERC, and state PUC reviews.
Continuous evaluation catches drift and surfaces new patterns. Operators tag novel events. The next retrain incorporates them. The system gets smarter over time, not staler.
