Aranya Chakrabortty
Biography
My research interests span all branches of control system theory with applications to large-scale electric power systems. I am a part of the FREEDM Systems Center, currently researching several system and control-theoretic problems for the US power grid using Synchrophasor (WAMS) technology, and its integration with renewable energy sources such as wind energy.
Education
-
Ph.D.
2008
Electrical Engineering
Rensselaer Polytechnic Institute, NY -
Master's
2005
Electrical Engineering
Rensselaer Polytechnic Institute, NY -
Bachelor's
2004
Electrical Engineering
Jadavpur University, India
Research Focus
- Communications and Signal Processing
- Control, Robotics, and Mechatronics
- Power Electronics and Power Systems
Funded Research
- Risk Segmentation And Portfolio Analysis For Pareto Dominance In High Renewable Penetration And Storage Reserves
- Intergovernmental Personnel Act Agreement for Aryanya Chakrabortty
- Cybersecurity for Electric Power Systems
- CPS: Small: Data-Driven Reinforcement Learning Control of Large CPS Networks using Multi-Stage Hierarchical Decompositions
- EAGER: Data-Driven Control of Power Systems Using Structured Reinforcement Learning
- Wide-Area Control of New York State Power Grid using FACTS and Wind Farms, FREEDM Core project
- Proposal DM-02: Identification and Mitigation of Coordinated Attacks on Distributed Energy Management, CAPER Core Project
- Hierarchical Control and Information Sharing Methods for Next-Generation Inverter-Interfaced Power Transmission Networks
- EAGER: Collaborative Research: Spatially Continuous Modeling of Power System Oscillations with Renewable Energy Penetration
- Retrofit Control: A New, Modular Gyrator Control Approach for Integrating Large-Scale Renewable Power
- CPS: TTP Option: Synergy: Collaborative Research: Hardening Network Infrastructures for Fast, Resilient, and Cost-Optimal Wide-Area Control of Power Systems
- US Ignite: Track 1: Collaborative Research: DISTINCT: A Distributed Multi-Loop Networked System for Wide-Area Control of Large Power Grids
- Collaborative Research: Computational Methods for Stability Assessment of Power Systems with High Penetration of Clean Renewal Energy
Highlighted Awards
- NSF CAREER Award (2011)
- University Faculty Scholars (2019)
Awards & Honors
- 2019 - University Faculty Scholar
- 2018 - Editor of IEEE Transactions on Power Systems
- 2016 - Associate Editor, IEEE Transactions on Control System Technology
- 2015 - Senior Member of IEEE
- 2011 - NSF CAREER Award
- 2009 - Allen B Dumont Prize (RPI, Top PhD Graduate)
- 2006 - HKN Honor Society award for best Graduate TA, RPI
Recent News
2020 Promoted and Tenured Faculty
Posted on November 12, 2020 | Filed Under: Faculty
Congratulations to five outstanding ECE faculty members who were promoted to Professor or granted tenure this year.
Managing data flow to boost cyber-physical system performance
Posted on November 11, 2020 | Filed Under: Research
Optimizing data flow allows ECE researchers to dramatically improve the performance of cyber-physical systems, from autonomous vehicles to smart power grids.
Research Advances Learning Capability of Drone Swarms
Posted on August 31, 2020 | Filed Under: Research
Researchers, including Aranya Chakrabortty, have developed a reinforcement learning approach that will allow swarms of unmanned vehicles to accomplish various missions while minimizing performance uncertainty.
Recent Media Mentions
Army advances learning capabilities of drone swarms
August 10, 2020
NCSU and Johns Hopkins team up to stabilize power grid
January 3, 2014
Researchers have found that an increase in the use of wind power generation can make the power grid more fragile and susceptible to disruptions. Aranya Chakrabortty, electrical engineering, featured.
Wind energy may endanger the grid
January 8, 2014
Researchers identify a problem concerning the intermittency related to wind energy generation. Aranya Chakrabortty, electrical engineering, featured.
Recent Publications
- Learning Distributed Stabilizing Controllers for Multi-Agent Systems (2022)
- Decomposability and Parallel Computation of Multi-Agent LQR (2021)
- EFFICIENT ALGORITHMS FOR EIGENSYSTEM REALIZATION USING RANDOMIZED SVD (2021)
- Enhancing Controllability of Wind Farms Against Parametric Resonance: A Series Compensation Approach (2021)
- Fast Online Reinforcement Learning Control Using State-Space Dimensionality Reduction (2021)
- Model-Free Optimal Control of Linear Multiagent Systems via Decomposition and Hierarchical Approximation (2021)
- Model-based and model-free designs for an extended continuous-time LQR with exogenous inputs (2021)
- Modeling and Quantifying the Impact of Wind Penetration on Slow Coherency of Power Systems (2021)
- Optimal Power Flow Design for Enhancing Dynamic Performance: Potentials of Reactive Power (2021)
- Reduced-dimensional reinforcement learning control using singular perturbation approximations (2021)