An Internet-of-Things (IoT) and machine-learning solution to combat energy loss by identifying power line tapping and meter tampering in real time.
Key Highlights
- Hardware Sensing Layer: Utilized ESP32 microcontrollers paired with precision non-invasive CT current sensors and AC voltage sensors to gather continuous power telemetry.
- Machine Learning Analysis: Trained classification algorithms on historical consumption profiles to accurately detect line power imbalances and anomalous usage.
- Backend Data Pipeline: Real-time telemetry ingestion pipeline that processes raw readings, computes phase differences, and flags suspicious power draw.
- Operator Dashboard: Interactive web interface displaying live grid statistics, phase current comparisons, and automated alert dispatching for field inspection.
- Multidisciplinary Engineering: Spanned embedded firmware, IoT communication protocols, predictive analytics, and cloud infrastructure.