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

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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
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 4, Pages: 1618-1631(2024)
- 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
  CLC：
- Published：01 July 2024
- 稿件说明：
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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：

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.

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Related Institution

School of Electrical and Electronic Engineering, Nanyang Technological University

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State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronics Engineering, Huazhong University of Science and Technology

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