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

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

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
- Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第1期页码：197-208
- 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
  中图分类号：
- 纸质出版：2023-01-01
- 稿件说明：
移动端阅览
Hongtai Zeng, Yanzhen Zhou, Qinglai Guo, 等. Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(1):197-208.

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.
Hongtai Zeng, Yanzhen Zhou, Qinglai Guo, 等. Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(1):197-208. DOI： 10.17775/CSEEJPES.2020.04610.

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.

关键词

Keywords

references

浏览量

101

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Advances in Transient Stability-Constrained Optimal Power Flow of Power Systems

Data-Driven Convex Optimal Power Flow Approximation for Uncertainty-Aware Static Voltage Stability Margin Calculation

Transient Stability Enhancement of Grid-forming Inverters with Controllable Frequency Deviation via Acceleration Power Control

Dynamic Energy Model and Sub-Synchronous Oscillation Mechanism for Direct-Drive Wind Farm Considering Interaction Between Generators

Enhanced Transient Stability Strategy for Grid-Forming Converter Based on Current Limiting