Di Shi
Cited by
Cited by
Deep-reinforcement-learning-based autonomous voltage control for power grid operations
J Duan, D Shi, R Diao, H Li, Z Wang, B Zhang, D Bian, Z Yi
IEEE Transactions on Power Systems 35 (1), 814-817, 2019
Modeling, control, and protection of modular multilevel converter-based multi-terminal HVDC systems: A review
L Zhang, Y Zou, J Yu, J Qin, V Vittal, GG Karady, D Shi, Z Wang
CSEE Journal of Power and Energy Systems 3 (4), 340-352, 2017
A data-driven multi-agent autonomous voltage control framework using deep reinforcement learning
S Wang, J Duan, D Shi, C Xu, H Li, R Diao, Z Wang
IEEE Transactions on Power Systems 35 (6), 4644-4654, 2020
A Monte Carlo simulation approach to evaluate service capacities of EV charging and battery swapping stations
T Zhang, X Chen, Z Yu, X Zhu, D Shi
IEEE Transactions on Industrial Informatics 14 (9), 3914-3923, 2018
Identification of short transmission-line parameters from synchrophasor measurements
D Shi, DJ Tylavsky, N Logic, KM Koellner
2008 40th North American Power Symposium, 1-8, 2008
Reinforcement-learning-based optimal control of hybrid energy storage systems in hybrid AC–DC microgrids
J Duan, Z Yi, D Shi, C Lin, X Lu, Z Wang
IEEE Transactions on Industrial Informatics 15 (9), 5355-5364, 2019
Transmission line parameter identification using PMU measurements
D Shi, DJ Tylavsky, KM Koellner, N Logic, DE Wheeler
European Transactions on Electrical Power 21 (4), 1574-1588, 2011
An Adaptive Method for Detection and Correction of Errors in PMU Measurements
D Shi, DJ Tylavsky, N Logic
IEEE Transactions on Smart Grid 3 (4), 1575-1583, 2012
Autonomous voltage control for grid operation using deep reinforcement learning
R Diao, Z Wang, D Shi, Q Chang, J Duan, X Zhang
2019 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2019
Optimization of battery energy storage to improve power system oscillation damping
Y Zhu, C Liu, K Sun, D Shi, Z Wang
IEEE Transactions on Sustainable Energy 10 (3), 1015-1024, 2018
A blockchain-enabled multi-settlement quasi-ideal peer-to-peer trading framework
MK AlAshery, Z Yi, D Shi, X Lu, C Xu, Z Wang, W Qiao
IEEE Transactions on Smart Grid 12 (1), 885-896, 2020
Online identification and data recovery for PMU data manipulation attack
X Wang, D Shi, J Wang, Z Yu, Z Wang
IEEE Transactions on Smart Grid 10 (6), 5889-5898, 2019
Thermostatic load control for system frequency regulation considering daily demand profile and progressive recovery
Q Shi, F Li, G Liu, D Shi, Z Yi, Z Wang
IEEE Transactions on Smart Grid 10 (6), 6259-6270, 2019
Small-signal stability analysis and performance evaluation of microgrids under distributed control
Y Yan, D Shi, D Bian, B Huang, Z Yi, Z Wang
IEEE Transactions on Smart Grid 10 (5), 4848-4858, 2018
Electric vehicles participation in load frequency control based on mixed H2/H∞
M Khan, H Sun, Y Xiang, D Shi
International Journal of Electrical Power & Energy Systems 125, 106420, 2021
A distributed cooperative control framework for synchronized reconnection of a multi-bus microgrid
D Shi, X Chen, Z Wang, X Zhang, Z Yu, X Wang, D Bian
IEEE Transactions on Smart Grid 9 (6), 6646-6655, 2017
Consensus-based distributed cooperative control for microgrid voltage regulation and reactive power sharing
D He, D Shi, R Sharma
IEEE PES Innovative Smart Grid Technologies, Europe, 1-6, 2014
A deep reinforcement learning-based multi-agent framework to enhance power system resilience using shunt resources
M Kamruzzaman, J Duan, D Shi, M Benidris
IEEE Transactions on Power Systems 36 (6), 5525-5536, 2021
Optimal allocation of series FACTS devices under high penetration of wind power within a market environment
X Zhang, D Shi, Z Wang, B Zeng, X Wang, K Tomsovic, Y Jin
IEEE Transactions on power systems 33 (6), 6206-6217, 2018
A novel bus-aggregation-based structure-preserving power system equivalent
D Shi, DJ Tylavsky
IEEE Transactions on Power Systems 30 (4), 1977-1986, 2014
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