Cybersecurity via Inverter-Grid Automatic Reconfiguration (CIGAR)

This project is performing R&D to enable distribution grids to adapt to resist a cyber-attack by (1) developing adaptive control algorithms for DER, voltage regulation, and protection systems; (2) analyze new attack scenarios and develop associated defensive strategies. It is funded by DOE CESER’s CEDS program and is co-led by Sean Peisert and Daniel Arnold.

Integrated Multi Scale Machine Learning for the Power Grid

The goal of this project is to create advanced, distributed data analytics capability to provide visibility and controllability to distribution grid operators. It is funded by the DOE Grid Modernization Initiative. The LBNL portion of this effort is led by Sean Peisert.

Power Grid Threat Detection and Response with Data Analytics

The goal of this project is to develop technologies and methodologies to protect the nation’s power grid from advanced cyber and all-hazard threats. This will be done through the collection of disparate data and the use of advanced analytics to detect threats and response to them. It is funded by DOE OE’s CEDS program via the Grid Modernization Initiative and is co-led by Sean Peisert.

Inferring Computing Activity Using Physical Sensors

This project uses power data to monitor the use of computing systems, including supercomputers and large computing centers. It is led by Sean Peisert.

Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA

This project is using micro-PMU measurements and SCADA commands to develop a system to detect cyberattacks against the power distribution grid. It is funded by DOE OE’s CEDS program and is led by Sean Peisert.