| 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 | 58 | 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 |
Michael (Misha) ChertkovChair, Program in Applied Mathematics, University of Arizonamath.arizona.edu의 이메일 확인됨