Adaptive Emergency Control of Power Systems Based on Deep Belief Network

Junyong Wu; Baoqin Li; Liangliang Hao; Fashun Shi; Pengjie Zhao

doi:10.17775/CSEEJPES.2022.00070

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 >

Adaptive Emergency Control of Power Systems Based on Deep Belief Network

Regular Papers | 更新时间：2026-10-15

- Adaptive Emergency Control of Power Systems Based on Deep Belief Network
- Adaptive Emergency Control of Power Systems Based on Deep Belief Network
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第4期页码：1618-1631
- 作者机构：
  
  Department of Electrical Engineering, Beijing Jiaotong University,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.00070
  中图分类号：
- 纸质出版：2024-07-01
- 稿件说明：
移动端阅览
Junyong Wu, Baoqin Li, Liangliang Hao, 等. Adaptive Emergency Control of Power Systems Based on Deep Belief Network[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(4):1618-1631.

Junyong Wu, Baoqin Li, Liangliang Hao, et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network[J]. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1618-1631.
Junyong Wu, Baoqin Li, Liangliang Hao, 等. Adaptive Emergency Control of Power Systems Based on Deep Belief Network[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(4):1618-1631. DOI： 10.17775/CSEEJPES.2022.00070.

Junyong Wu, Baoqin Li, Liangliang Hao, et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network[J]. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1618-1631. DOI： 10.17775/CSEEJPES.2022.00070.

摘要

Abstract

Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation

online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges

this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First

a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted

which takes into account accuracy and rapidity at the same time. Next

a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen

optimal emergency control strategy can be determined in time. Lastly

responses after emergency control are fed back to the TSP model. If prediction result is still unstable

an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning

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

Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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

Coordinated modulation strategy considering multi-HVDC emergency for enhancing transient stability of hybrid AC/DC power systems