Deepjyoti Deka

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Michael (Misha) ChertkovChair, Program in Applied Mathematics, University of ArizonaAdresse e-mail validée de math.arizona.edu
Scott BackhausNational Institute of Standards and TechnologyAdresse e-mail validée de nist.gov
M. SalapakaUniversity of Minnesota, Twin CitiesAdresse e-mail validée de umn.edu
Sriram VishwanathUniversity of Texas, MITREAdresse e-mail validée de mitre.org
saurav talukdarVerrus LLCAdresse e-mail validée de umn.edu
Wenting LiLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Sidhant MisraScientist, Applied Mathematics Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Uzi (Yury) DvorkinJohns Hopkins UniversityAdresse e-mail validée de jhu.edu
Ana BusicInria, Computer Science Department at École normale supérieureAdresse e-mail validée de ens.fr
Vassilis KekatosPurdue UniversityAdresse e-mail validée de purdue.edu
Yury MaximovLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Md Umar HashmiResearch Expert, KU Leuven & EnergyVilleAdresse e-mail validée de KULEUVEN.BE
Kaarthik SundarScientist, Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Harsha NagarajanScientist, Theoretical Division (Applied Mathematics), Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Andrey LokhovLos Alamos National Laboratory, Theoretical DivisionAdresse e-mail validée de lanl.gov
Lucas PereiraITI, LARSyS, Instituto Superior Técnico, Universidade de LisboaAdresse e-mail validée de tecnico.ulisboa.pt
Sejun ParkAssistant Professor, Korea UniversityAdresse e-mail validée de korea.ac.kr
Yize ChenAssistant Professor, University of AlbertaAdresse e-mail validée de ualberta.ca
Meng WangAssociate Professor, Rensselaer Polytechnic InstituteAdresse e-mail validée de rpi.edu
Petr VorobevSenior Research Fellow, Nanyang Technological UniversityAdresse e-mail validée de ntu.edu.sg

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Deepjyoti Deka

Research Scientist, MIT

Adresse e-mail validée de mit.edu - Page d'accueil

machine learning power systems control theory optimization cyber security


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Structure Learning in Power Distribution Networks D Deka, S Backhaus, M Chertkov IEEE Transactions on Control of Network Systems, 2017	274*	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	212	2019
Is machine learning in power systems vulnerable? Y Chen, Y Tan, D Deka 2018 IEEE International Conference on Communications, Control, and Computing …, 2018	118	2018
Learning for DC-OPF: Classifying active sets using neural nets D Deka, S Misra 2019 IEEE Milan PowerTech, 1-6, 2019	107	2019
Estimating distribution grid topologies: A graphical learning based approach D Deka, S Backhaus, M Chertkov 2016 Power Systems Computation Conference (PSCC), 1-7, 2016	107	2016
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	93	2018
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	84	2019
Big Data Application in Power Systems R Arghandeh, Y Zhou Big Data Application in Power Systems, 480, 2018	70	2018
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	64	2019
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	60	2020
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	57	2020
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	57	2016
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	55	2016
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	49	2018
Ensemble Control of Cycling Energy Loads: Markov Decision Approach M Chertkov, VY Chernyak, D Deka Energy Markets and Responsive Grids, 363-382, 2018	46	2018
Learning Distribution Grid Topologies: A Tutorial D Deka, V Kekatos, G Cavraro IEEE Transactions on Smart Grid, 2022	45	2022
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	44	2020
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	40	2017
Stochastic AC optimal power flow: A data-driven approach I Mezghani, S Misra, D Deka Electric Power Systems Research 189, 106567, 2020	38	2020
Chance-constrained ADMM approach for decentralized control of distributed energy resources A Hassan, Y Dvorkin, D Deka, M Chertkov 2018 Power Systems Computation Conference (PSCC), 1-7, 2018	38	2018

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