Accomplishments
Publications
- Nandanoori S., S. Guan, S. Kundu, S. Pal, K. Agarwal, Y. Wu, and S. Choudhury. "Data-driven Methods for Robust Prediction of Power System Transients." IEEE Transactions on Power Systems (Journal – under review)
- S. Kundu, S. Nandanoori, S. Pal, K. Agarwal, S. Sinha, and S. Choudhury. “PowerDrone: Adaptive Steering of Power Systems for Resilient Operation under Adversarial Conditions.” July, 2021. PNNL-31549. Richland, WA: Pacific Northwest National Laboratory.
- Nandanoori S., S. Pal, S. Sinha, S. Kundu, K. Agarwal, and S. Choudhury. 2021. "Data-driven Distributed Learning of Multi-agent Systems: A Koopman Operator Approach." In IEEE Control and Decision Conference. PNNL-SA-160981.
- Nandanoori S., S. Kundu, S. Pal, K. Agarwal, and S. Choudhury. 2020. "Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes." In IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2020), November 11-13, 2020, Tempe AZ, 1-6. Piscataway, New Jersey:IEEE. PNNL-SA-154191.
Presentations
- Kundu S., S. Nandanoori S., S. Pal, K. Agarwal, and S. Choudhury. 07/21/2020 "Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes" at the Virtual Workshop on Distribution and Transmission System Monitoring.
- Nandanoori S., S. Kundu, S. Pal, K. Agarwal, and S. Choudhury. 2020. "Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes." presented at the IEEE SmartGridComm 2020, November 11-13, 2020, Tempe AZ.
- Nandanoori S., S. Kundu, S. Pal, K. Agarwal, and S. Choudhury. 07/21/2020. "Framework for Large-Scale Data Generation for Designing Machine Learning Based Controls - GridSTAGE." Presented by S. Nandanoori at TechFest 2020, Online Conference, Richland, WA.
Repositories
- Adversarial scenario generation framework released (04/20): https://github.com/pnnl/GridSTAGE
- Developed and published benchmark dataset for developing and testing ML-driven predictive models for power grid: https://github.com/pnnl/GridSTAGE/tree/master/benchmarks/prediction
Invention Disclosures
- Patent initiated on “PowerDrone Simulation Framework” IP ID No. 31853-E.
- Patent initiated on “Generation and Detection of Stealth Measurement based Attacks for Cyber-Physical Systems” IP ID No. 31983.