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Deepjyoti Deka
Deepjyoti Deka
Research Scientist, MIT
Bestätigte E-Mail-Adresse bei mit.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Structure Learning in Power Distribution Networks
D Deka, S Backhaus, M Chertkov
IEEE Transactions on Control of Network Systems, 2017
280*2017
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
W Li, D Deka, M Chertkov, M Wang
IEEE Transactions on Power Systems 34 (6), 4640-4651, 2019
2132019
Is machine learning in power systems vulnerable?
Y Chen, Y Tan, D Deka
2018 IEEE International Conference on Communications, Control, and Computing …, 2018
1182018
Learning for DC-OPF: Classifying active sets using neural nets
D Deka, S Misra
2019 IEEE Milan PowerTech, 1-6, 2019
1122019
Estimating distribution grid topologies: A graphical learning based approach
D Deka, S Backhaus, M Chertkov
2016 Power Systems Computation Conference (PSCC), 1-7, 2016
1072016
Exact topology and parameter estimation in distribution grids with minimal observability
S Park, D Deka, M Chcrtkov
2018 power systems computation conference (PSCC), 1-6, 2018
932018
Topology estimation using graphical models in multi-phase power distribution grids
D Deka, M Chertkov, S Backhaus
IEEE Transactions on Power Systems 35 (3), 1663-1673, 2019
852019
Big Data Application in Power Systems
R Arghandeh, Y Zhou
Big Data Application in Power Systems, 480, 2018
672018
Designing reactive power control rules for smart inverters using support vector machines
M Jalali, V Kekatos, N Gatsis, D Deka
IEEE Transactions on Smart Grid 11 (2), 1759-1770, 2019
632019
Graphical models in meshed distribution grids: Topology estimation, change detection & limitations
D Deka, S Talukdar, M Chertkov, MV Salapaka
IEEE Transactions on Smart Grid 11 (5), 4299-4310, 2020
592020
Arbitrage with power factor correction using energy storage
MU Hashmi, D Deka, A Bušić, L Pereira, S Backhaus
IEEE Transactions on Power Systems 35 (4), 2693-2703, 2020
592020
Learning topology of distribution grids using only terminal node measurements
D Deka, S Backhaus, M Chertkov
2016 IEEE International Conference on Smart Grid Communications …, 2016
582016
Learning Distribution Grid Topologies: A Tutorial
D Deka, V Kekatos, G Cavraro
IEEE Transactions on Smart Grid, 2022
562022
Learning topology of the power distribution grid with and without missing data
D Deka, S Backhaus, M Chertkov
2016 European Control Conference (ECC), 313-320, 2016
552016
Optimal load ensemble control in chance-constrained optimal power flow
A Hassan, R Mieth, M Chertkov, D Deka, Y Dvorkin
IEEE Transactions on Smart Grid 10 (5), 5186-5195, 2018
492018
Learning with end-users in distribution grids: Topology and parameter estimation
S Park, D Deka, S Backhaus, M Chertkov
IEEE Transactions on Control of Network Systems 7 (3), 1428-1440, 2020
462020
Ensemble Control of Cycling Energy Loads: Markov Decision Approach
M Chertkov, VY Chernyak, D Deka
Energy Markets and Responsive Grids, 363-382, 2018
462018
Analytical Models for Power Networks: The case of the Western US and ERCOT grids
D Deka, S Vishwanath, R Baldick
IEEE Transactions on Smart Grid 8 (6), 2794--2802, 2017
402017
Stochastic AC optimal power flow: A data-driven approach
I Mezghani, S Misra, D Deka
Electric Power Systems Research 189, 106567, 2020
392020
Exact topology reconstruction of radial dynamical systems with applications to distribution system of the power grid
S Talukdar, D Deka, D Materassi, M Salapaka
2017 American Control Conference (ACC), 813-818, 2017
392017
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