W. Li, J. Lv, G. Shi, X. Cai and Y. Chi, "Improved AC fault ride through control strategy for MTDC system with offshore wind farms," 2014 International Conference on Power System Technology, Chengdu, 2014, pp. 2409-2419.
- Rensselaer Polytechnic Institute (RPI), Troy, NY, 2015-2019
- Ph.D in Electrical Engineering
- Rensselaer Polytechnic Institute (RPI), Troy, NY, 2017-2019
- Master in Applied Mathematics
- Harbin Institute of Technology (HIT), Harbin, China, 2009-2013
- B.Sc. in Electrical Engineering
- Summer Intern in 2018:
- Los Alamos National Laboratory (LANL), NM
- Research Topic: Real-time Fault Location through Convolutional Neural Network
- Duties: Propose a real-time approach to locate faults in a network with high accuracy even only 7% of network nodes are observed, while other methods require 30% of nodes to be observed. Develop a node selection algorithm to determine the measured nodes, increasing 10% location accuracy.
- Supervisor: Michael Chertkov, Deepjyoti Deka
- Python, Matlab, R, C, Machine/deep learing, Approximation algorithms, Statistics, Optimization
- Tensorflow, Pytorch, Jupyter Notebook, Latex, Scikit-learn, CVX, AMPL, PSCAD, PSSE
- Language: Chinese (native) and English
A novel DC voltage control strategy for multiterminal HVDC system with offshore wind farms integration
W. Li, G. Shi, X. Cai and N. Li, "A novel DC voltage control strategy for multiterminal HVDC system with offshore wind farms integration," 2014 International Power Electronics and Application Conference and Exposition, Shanghai, 2014, pp. 1110-1115.
W. Li, M. Wang and J. H. Chow, "Fast event identification through subspace characterization of PMU data in power systems," 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, 2017, pp. 1-5.
Wang, Meng, et al. "Recent Results of PMU Data Analytics by Exploiting Low-dimensional Structures." Proc. of the 10th Bulk Power Systems Dynamics and Control Symposium (IREP), Espinho, Portugal. 2017.
Real-Time Event Identification Through Low-Dimensional Subspace Characterization of High-Dimensional Synchrophasor Data
W. Li, M. Wang and J. H. Chow, "Real-Time Event Identification Through Low-Dimensional Subspace Characterization of High-Dimensional Synchrophasor Data," in IEEE Transactions on Power Systems, vol. 33, no. 5, pp. 4937-4947, Sept. 2018.
W. Li and M. Wang, "Identifying Overlapping Successive Events Using a Shallow Convolutional Neural Network," in IEEE Transactions on Power Systems.
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
W. Li, D. Deka, M. Chertkov and M. Wang, "Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks," in IEEE Transactions on Power Systems.
M. Wang et al., "A Low-rank Framework of PMU Data Recovery and Event Identification," 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), College Station, TX, USA, 2019, pp. 1-9.
W. Li, M. Yi, M. Wang, Y. Wang, D. Shi and Z. Wang, "Real-time Energy Disaggregation at Substations with Behind-the-Meter Solar Generation," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3035639.
Li W, Deka D. Physics Informed Neural Networks for High Impedance Faults Detection. arXiv preprint arXiv:2008.02364, 2020.
Course Project at Rensselaer Polytechnics Institute, Troy NY, USA,
Rewards and Service
- Founders Award of Excellence, 2018 (top 1%)
- North America Finalist of IBM Watson Build Challenge, 2017
- The excellent new PhD Student Scholarship, 2013 (Top 1%)
- Peoples Scholarship (Top 3%) & National Encouragement Scholarship, 2012 (Top 2%)
- Honorable Mention Award of Mathematical Modeling, 2012
- Member of smart grid publication committee
- Reviewer of Smart Grid Transaction Journal
- Reviewer of Power System Transaction Journal
- Reviewer of Power Energy Scociety General Meeting