TY - GEN
T1 - Deep Reinforcement Leaming for Short-term Voltage Control by Dynamic Load Shedding in China Southem Power Grid
AU - Zhang, Jingyi
AU - Lu, Chao
AU - Si, Jennie
AU - Song, Jie
AU - Su, Yinsheng
N1 - Funding Information:
Foundation of China (U1766214). Si’s work was supported in part by the NSF 978-1-5090-6014-6/18/$31.00 ©2018 IEEE under grant IIS-1563454.
Funding Information:
Program of China (2017YFB0902800) and in part by National Natural Science
Funding Information:
This work was supported in part by National Key Research and Development
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - We propose a novel load shedding (LS) scheme against voltage instability using deep reinforcement learning (DRL). Both spatial and temporal information of a large power grid are used in the DRL control scheme. Specifically, both dynamic state variables and grid topology information are inputs to the learning controller. Within the DRL scheme, a deep learning neural network is designed and implemented to automatically extract translation-invariance information about voltage instability. The DRL load shedding controller interacts with system dynamics through a sequence of observations, actions and rewards to determine load shedding amounts in a manner that maximizes cumulative future reward, accomplishing coordination within the region rapidly to meet the online application requirements. The DRL based distributed LS scheme is performed to control the China Southern Power Grid (CSG) system. Our results show improved voltage recovery performance by load shedding using the proposed scheme under different unknown test scenarios.
AB - We propose a novel load shedding (LS) scheme against voltage instability using deep reinforcement learning (DRL). Both spatial and temporal information of a large power grid are used in the DRL control scheme. Specifically, both dynamic state variables and grid topology information are inputs to the learning controller. Within the DRL scheme, a deep learning neural network is designed and implemented to automatically extract translation-invariance information about voltage instability. The DRL load shedding controller interacts with system dynamics through a sequence of observations, actions and rewards to determine load shedding amounts in a manner that maximizes cumulative future reward, accomplishing coordination within the region rapidly to meet the online application requirements. The DRL based distributed LS scheme is performed to control the China Southern Power Grid (CSG) system. Our results show improved voltage recovery performance by load shedding using the proposed scheme under different unknown test scenarios.
KW - Under voltage load shedding
KW - deep reinforcement learning
KW - spatial-temporal information fusion
UR - http://www.scopus.com/inward/record.url?scp=85056528897&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056528897&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489041
DO - 10.1109/IJCNN.2018.8489041
M3 - Conference contribution
AN - SCOPUS:85056528897
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
Y2 - 8 July 2018 through 13 July 2018
ER -