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. My current research interests also include the application of machine learning based controls to power system applications at both transmission and distribution levels.

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

Recent Publications

• Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent (2024)
• Climate Change Mitigation, Adaptation, and Resilience: Challenges and Opportunities for the Control Systems Community (2024)
• Distributed Multiagent Reinforcement Learning Based on Graph-Induced Local Value Functions (2024)
• Distributed Reinforcement Learning for Networked Dynamical Systems (2023)
• Optimal Charging Control and Incentivization Strategies for Electric Vehicles Considering Grid Dynamical Constraints (2024)
• Physics-aware Regression for DER Dispatch with Topological Reconfigurations of Radial Feeder (2024)
• A Generative Adversarial Network Approach for Identification and Mitigation of Cyber-Attacks in Wide-Area Control of Power Systems (2023)
• Data-Driven DER Dispatch in Distribution Networks using Multi-Stage Regression (2023)
• Data-Driven Optimal Power Dispatch for Distributed Energy Resources in Radial Feeder using Multi-Stage Regression (2023)
• Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems Based on Sample Identifying Complexity and Deciphering Time (2022)