Deep Reinforcement Leaming for Short-term Voltage Control by Dynamic Load Shedding in China Southem Power Grid

Jingyi Zhang, Chao Lu, Jennie Si, Jie Song, Yinsheng Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
StatePublished - Oct 10 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: Jul 8 2018Jul 13 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Other

Other2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period7/8/187/13/18

Keywords

  • Under voltage load shedding
  • deep reinforcement learning
  • spatial-temporal information fusion

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Zhang, J., Lu, C., Si, J., Song, J., & Su, Y. (2018). Deep Reinforcement Leaming for Short-term Voltage Control by Dynamic Load Shedding in China Southem Power Grid. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings [8489041] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489041