Sayak Mukherjee

I am interested in control of dynamic systems with applications related to Power and Energy Systems . My research interests are on Wind-integrated power system modeling, analysis and control, Power systems dynamic performance, Wide area monitoring and control, Optimal and Robust control,  Adaptive dynamic programming (ADP)/Reinforcement Learning (RL) and Power Electronics for renewable integration.

Recent Publications-

5. S. Mukherjee, A. Darvishi, A. Chakrabortty, and B. Fardanesh, Learning Power System Dynamic Signatures using LSTM-Based Deep Neural Network: A Prototype Study on the New York State Grid. IEEE PES General Meeting, Atlanta, GA, 2019.

4.  S. Mukherjee, A. Chakrabortty, and H. Bai. Block-Decentralized Model-Free Reinforcement Learning Control of Two Time-Scale Networks. American Control Conference, Philadelphia, 2019.

3. S. Mukherjee, H. Bai, and A. Chakrabortty. On Model-Free Reinforcement Learning of Reduced-order Optimal Control for Singularly Perturbed Systems.  59th IEEE Conference on Decision and Control, FL, Dec. 2018.

2. S. Mukherjee, N. Xue, and A. Chakrabortty. A Hierarchical Design for Damping Control of Wind-Integrated Power Systems Considering Heterogeneous Wind Farm Dynamics.  2nd IEEE Conference on Control Technology and Applications, Denmark, Aug. 2018.

1. S. Mukherjee, S. Babaei, and A. Chakrabortty. A Measurement-based Approach for Optimal Damping Control of the New York State Power Grid. IEEE PES General Meeting, Portland, OR, 2018.

Title

Graduate Research Asst

Office

Keystone Science Center 100

Type of DegreeDegree ProgramSchoolYear
B.E.Electrical EngineeringJadavpur University, India2015

Research Areas

  • Power Systems

Publications