PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE FOR MICROGRID INSIGHTS
Microgrid state vector by fusing µPMU, SCADA, AMI and DER data
As part of a DOE-funded collaboration with Argonne National Laboratory:
- Developing AI/ML algorithm for enhanced real-time visibility and monitoring of microgrids by creating a data fusion framework for synchronizing and harmonizing multi-modal sensor data at different time resolutions and training our algorithms on the fused data.
- processes raw µPMU, AMI, SCADA, and DER data streams.
- AI/ML Design and Training
- Validation and Testing
- Anomaly Detection
- Creating Dashboards
- Prediction of constraint violations.