I am interested in the control of dynamical systems using tools from classical control theory, reinforcement learning, and optimization theory with applications related to power and energy systems. My research interests are on the techniques from optimal and robust control, reinforcement earning (RL)/ adaptive dynamic programming (ADP) for continuous-time dynamic systems, RL for sequential decision making with applications related to power systems, distributed energy resources (DERs) etc. Recently I am also working in the areas of probabilistic modeling, Bayesian inference, graph neural networks, and resilient control designs. I did my Ph.D. under the direction of Dr. Aranya Chakrabortty at North Carolina State University. I have joined PNNL as a post-doctorate research associate in the optimization and control group and currently hold the research scientist position.
My Ph.D. dissertation under the direction of Dr. Aranya Chakrabortty:
S. Mukherjee, Ph.D. Dissertation, Data-Driven Reinforcement Learning Control using Model Reduction Techniques: Theory and Applications to Power Systems, 2020, available via NC State Libraries, https://www.lib.ncsu.edu/resolver/1840.20/37368.
Recent Talks-
5. Featured lightning talk at Duke University Energy Data Analytics Symposium on “Scalable Reinforcement Learning-based Control of Distributed Energy Resources”, 2020.
4. FREEDM technical tutorial on RL Control for Power Systems (.ppt at the end of the page), 2020.
3. Invited talk at LIDS, MIT on “Reinforcement Learning Control using Dimensionality Reduction and Applications to Power System Dynamics”, 2020.
2. Invited student talk on “Reduced-dimensional Reinforcement Learning Control for Time-scale Separated Dynamical Systems” in Southeast Controls Conference 2019 at Georgia Tech.
1. Invited talk on “Reinforcement Learning Based Wide-Area Control of Power Systems Using Dimensionality Reduction Techniques” on behalf of Dr. Aranya Chakrabortty in 2019 Conference on Information Sciences and Systems (CISS), Johns Hopkins University, MD.
Awards and Recognitions –
2. Outstanding Performance Award, Energy and Environment Directorate, Pacific Northwest National Laboratory.
1. Medal for second-highest percentage (with highest GPA) in B.E., Jadavpur University, India.
Recent Publications-
Journals:
J7. S. Mukherjee, H. Bai, A. Chakrabortty, “Model-based and Model-free Designs for an Extended Continuous-time LQR with Exogenous Inputs”, Systems and Control Letters, Elsevier, 2021.
J6. S. Mukherjee, H. Bai, A. Chakrabortty, “Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations”, Automatica, 2021.
J5. S. Mukherjee, R. Huang, Q. Huang, T.L. Vu, T. Yin, “Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning”, submitted, arXiv preprint arXiv:2102.00077, 2021.
J4. S. Mukherjee, T.L. Vu, “On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee”, IEEE Control Systems Letters, 2020.
J3. S. Mukherjee, A. Chakrabortty, H. Bai, A. Darvishi, B. Fardanesh, “Scalable Designs for Reinforcement Learning-based Wide-Area Control”, IEEE Transactions on Smart Grid, 2020.
J2. S. Mukherjee, A. Chakrabortty, S. Babaei, “Modeling and Quantifying the Impact of Wind Power Penetration on Slow Coherency of Power Systems”, IEEE Trans. on Power Systems, 2020.
J1. S. Mukherjee, S. Babaei, A. Chakrabortty, B. Fardanesh “Designing a Measurement-driven Optimal Controller for an Utility-Scale Power System: A New York State Grid Perspective”, International Journal of Power and Energy Systems, Elsevier, 2020.
Conferences:
C13. Jiaxin Zhang, Jan Drgona, Sayak Mukherjee, Mahantesh Halappanavar, Frank Liu, “Variational Generative Flows for Reconstruction Uncertainty Estimation”, ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning.
C12. T.L. Vu, S. Mukherjee, R. Huang, J. Tan, Q. Huang,“Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems”, Conference on Decision and Control, 2021.
C11. S. Mukherjee, T.L. Vu, “On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee”, American Control Conference (L-CSS presentation), 2021.
C10. T.L. Vu, S. Mukherjee, T. Yin, R. Huang, J. Tan, Q. Huang,“Safe Reinforcement Learning for Emergency Load Shedding of Power Systems”, IEEE PES General Meeting, 2021.
C9. S. Mukherjee, V. Adetola, “A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamic Systems under Attacks”, IEEE CCTA, 2021.
C8. S. Mukherjee, H. Bai, and A. Chakrabortty, “Reinforcement Learning Control of Power Systems with Unknown Network Model under Ambient and Forced Oscillations”, invited paper in IEEE Conference on Control Technology and Applications (CCTA), Montreal, Canada, 2020.
C7. S. Mukherjee, H. Bai, A. Chakrabortty, “On Robust Reduced-Dimensional Reinforcement Learning Control for Singularly Perturbed Systems’ , American Control Conference, Denver, CO, 2020.
C6. S. Mukherjee, H. Bai, A. Chakrabortty, “Model-free Decentralized Reinforcement Learning Control for Distributed Energy Resources”, IEEE PES General Meeting, 2020.
C5. S. Mukherjee, H. Bai, A. Chakrabortty, “Block-Decentralized Model Free reinforcement Learning of Two-time Scale Networks”, American Control Conference, 2019.
C4. S. Mukherjee, A. Darvishi, A. Chakrabortty, 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.
C3. S. Mukherjee, H. Bai, A. Chakrabortty, “On Model-Free Reinforcement Learning for Singularly Perturbed Systems”, IEEE Conference on Decision and Control, Miami, Florida, 2018.
C2. S. Mukherjee, N. Xue, and A. Chakrabortty, “A Hierarchical Design for Damping Control of Wind-Integrated Power Systems Considering Heterogeneous Wind Farm Dynamics”, IEEE Conference on Control Technology and Applications, Denmark, 2018.
C1. 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 Degree | Degree Program | School | Year |
|---|---|---|---|
| B.E. | Electrical Engineering | Jadavpur University, India | 2015 |
| Ph.D. | Electrical Engineering | North Carolina State University, USA | 2020 |
Research Areas
- Power Systems