Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints

Hongtai Zeng; Yanzhen Zhou; Qinglai Guo; Zhongmin Cai; Hongbin Sun

doi:10.17775/CSEEJPES.2020.04610

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Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints

Regular Papers | 更新时间：2026-10-15

- Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints
- CSEE Journal of Power and Energy Systems Vol. 9, Issue 1, Pages: 197-208(2023)
- 作者机构：
  
  1. Department of Electrical Engineering, State Key Laboratory of Power Systems, Tsinghua University,Beijing,China
  2. Department of Automation Science and Technology, Xi'an Jiaotong University,Xi'an,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.04610
  CLC：
- Published：01 January 2023
- 稿件说明：
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Hongtai Zeng, Yanzhen Zhou, Qinglai Guo, et al. Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints[J]. CSEE Journal of Power and Energy Systems, 2023, 9(1): 197-208.
DOI：

Hongtai Zeng, Yanzhen Zhou, Qinglai Guo, et al. Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints[J]. CSEE Journal of Power and Energy Systems, 2023, 9(1): 197-208. DOI： 10.17775/CSEEJPES.2020.04610.

摘要

Abstract

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe contingencies. It is mathematically an optimal power flow problem with transient stability constraints. Due to the constraints involved for differential algebraic equations of transient stability

it is difficult and time-consuming to solve this problem. To address these issues

this paper presents a novel deep reinforcement learning (DRL) framework for preventive transient stability control of power systems. A distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid simulator. Once properly trained

the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating position with a near-optimal operational cost. The effectiveness of the proposed method is verified through numerical experiments conducted on a New England 39-bus system and NPCC 140-bus system.

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