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Rahul Nellikkath
Rahul Nellikkath
Ph.D Student at Department of Wind and Energy Systems DTU, B.Tech-M.Tech, Indian Institute of
Verified email at dtu.dk
Title
Cited by
Cited by
Year
Physics-informed neural networks for ac optimal power flow
R Nellikkath, S Chatzivasileiadis
Electric Power Systems Research 212, 108412, 2022
782022
Physics-informed neural networks for minimising worst-case violations in DC optimal power flow
R Nellikkath, S Chatzivasileiadis
2021 IEEE International Conference on Communications, Control, and Computing …, 2021
312021
Volt–Var optimization and reconfiguration: Reducing power demand and losses in a droop-based microgrid
Y Gupta, R Nellikkath, K Chatterjee, S Doolla
IEEE Transactions on Industry Applications 57 (3), 2769-2781, 2021
242021
Closing the loop: A framework for trustworthy machine learning in power systems
J Stiasny, S Chevalier, R Nellikkath, B Sævarsson, S Chatzivasileiadis
arXiv preprint arXiv:2203.07505, 2022
142022
Network-aware flexibility requests for distribution-level flexibility markets
E Prat, I Dukovska, R Nellikkath, M Thoma, L Herre, S Chatzivasileiadis
IEEE Transactions on Power Systems 39 (2), 2641-2652, 2023
42023
Minimizing worst-case violations of neural networks
R Nellikkath, S Chatzivasileiadis
arXiv preprint arXiv:2212.10930, 2022
32022
Volt—var optimization and reconfiguration–reducing power losses in a droop based microgrid
Y Gupta, R Nellikkath, K Chatterjee, S Doolla
2020 IEEE International Conference on Power Electronics, Smart Grid and …, 2020
32020
Enriching neural network training dataset to improve worst-case performance guarantees
R Nellikkath, S Chatzivasileiadis
2023 IEEE Belgrade PowerTech, 1-6, 2023
22023
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
R Nellikkath, A Venzke, MK Bakhshizadeh, I Murzakhanov, ...
arXiv preprint arXiv:2303.12116, 2023
22023
Interpretable machine learning for power systems: establishing confidence in shapley additive explanations
RI Hamilton, J Stiasny, T Ahmad, S Chevalier, R Nellikkath, ...
arXiv preprint arXiv:2209.05793, 2022
22022
Correctness Verification of Neural Networks Approximating Differential Equations
P Ellinas, R Nellikath, I Ventura, J Stiasny, S Chatzivasileiadis
arXiv preprint arXiv:2402.07621, 2024
12024
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
R Nellikkath, M Tanneau, P Van Hentenryck, S Chatzivasileiadis
arXiv preprint arXiv:2405.06109, 2024
2024
Trustworthy Machine Learning for Power System Applications
R Nellikkath
DTU Wind and Energy Systems, 2024
2024
Physics-Informed Neural Networks for AC Optimal Power Flow
R Nellikkath, S Chatzivasileiadis
arXiv preprint arXiv:2110.02672, 2021
2021
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