| 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 | 182 | 2019 |
| Real-time event identification through low-dimensional subspace characterization of high-dimensional synchrophasor data W Li, M Wang, JH Chow IEEE Transactions on Power Systems 33 (5), 4937-4947, 2018 | 55 | 2018 |
| 2022 review of data-driven plasma science R Anirudh, R Archibald, MS Asif, MM Becker, S Benkadda, PT Bremer, ... IEEE Transactions on Plasma Science, 2023 | 42 | 2023 |
| Characteristics of the fecal microbiota of high-and low-yield hens and effects of fecal microbiota transplantation on egg production performance Y Wang, L Xu, X Sun, X Wan, G Sun, R Jiang, W Li, Y Tian, X Liu, X Kang Research in veterinary science 129, 164-173, 2020 | 40 | 2020 |
| Identifying overlapping successive events using a shallow convolutional neural network W Li, M Wang IEEE Transactions on Power Systems 34 (6), 4762-4772, 2019 | 35 | 2019 |
| Real-time energy disaggregation at substations with behind-the-meter solar generation W Li, M Yi, M Wang, Y Wang, D Shi, Z Wang IEEE Transactions on Power Systems 36 (3), 2023-2034, 2020 | 34 | 2020 |
| Fast event identification through subspace characterization of PMU data in power systems W Li, M Wang, JH Chow 2017 IEEE Power & Energy Society General Meeting, 1-5, 2017 | 26 | 2017 |
| Physics-informed learning for high impedance faults detection W Li, D Deka 2021 IEEE Madrid PowerTech, 1-6, 2021 | 24 | 2021 |
| Probability model transforming encoders against encoding attacks H Cheng, Z Zheng, W Li, P Wang, CH Chu 28th USENIX Security Symposium (USENIX Security 19), 1573-1590, 2019 | 14 | 2019 |
| PPGN: Physics-preserved graph networks for real-time fault location in distribution systems with limited observation and labels W Li, D Deka arXiv preprint arXiv:2107.02275, 2021 | 9 | 2021 |
| Physics based gnns for locating faults in power grids W Li, D Deka Preprint at https://arxiv. org/abs/2107.02275, 2021 | 9 | 2021 |
| Physics-constrained adversarial training for neural networks in stochastic power grids W Li, D Deka, R Wang, MRA Paternina IEEE Transactions on Artificial Intelligence 5 (3), 1121-1131, 2023 | 8 | 2023 |
| Physics-conditioned generative adversarial networks for state estimation in active power distribution systems with low observability M Kamal, W Li, D Deka, H Mohsenian-Rad 2022 International Conference on Smart Grid Synchronized Measurements and …, 2022 | 5 | 2022 |
| Physics-informed graph learning for robust fault location in distribution systems W Li, D Deka arXiv e-prints, arXiv-2107, 2021 | 5 | 2021 |
| Improved AC fault ride through control strategy for MTDC system with offshore wind farms W Li, J Lv, G Shi, X Cai, Y Chi 2014 International Conference on Power System Technology, 2409-2419, 2014 | 4 | 2014 |
| A low-rank framework of pmu data recovery and event identification M Wang, JH Chow, Y Hao, S Zhang, W Li, R Wang, P Gao, C Lackner, ... 2019 International Conference on Smart Grid Synchronized Measurements and …, 2019 | 2 | 2019 |
| A novel DC voltage control strategy for multiterminal HVDC system with offshore wind farms integration W Li, G Shi, X Cai, N Li 2014 International Power Electronics and Application Conference and …, 2014 | 2 | 2014 |
| Ranking-Based Physics-Informed Line Failure Detection in Power Grids A Burashnikova, W Li, M Amini, D Deka, Y Maximov arXiv preprint arXiv:2209.01021, 2022 | 1 | 2022 |
| Physics regulated neural network for high impedance faults detection W Li, D Deka arXiv preprint arXiv:2008.02364, 2020 | 1 | 2020 |
| Event Identification Using Extracted Features from High-dimensional Power System Data W Li, M Wang 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 1853-1857, 2018 | 1 | 2018 |
Meng WangAssociate Professor, Rensselaer Polytechnic InstituteVerified email at rpi.edu
