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Regular Paper | Open Access

Adaptive Emergency Control of Power Systems Based on Deep Belief Network

Junyong Wu, Baoqin Li( ), Liangliang Hao, Fashun Shi, Pengjie Zhao
Department of Electrical Engineering, Beijing Jiaotong University, Beijing 100084, China
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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.

Keywords

Deep learningemergency controlpower systemsensitivitytransient stability prediction

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 4,
July 2024
Pages 1618-1631
DOI: 10.17775/CSEEJPES.2022.00070
Cite this article:
Wu J, Li B, Hao L, et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1618-1631. https://doi.org/10.17775/CSEEJPES.2022.00070

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Received: 16 January 2022
Revised: 08 March 2022
Accepted: 31 May 2022
Published: 28 December 2023
© 2022 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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