Adaptive Emergency Control of Power Systems Based on Deep Belief Network

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI:10.17775/CSEEJPES.2022.00070
Junyong Wu;Baoqin Li;Liangliang Hao;Fashun Shi;Pengjie Zhao
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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.
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基于深度信念网络的电力系统自适应应急控制
应急控制是帮助系统在故障排除后保持同步的重要手段。传统的 "离线计算、在线匹配 "方案在适应性和鲁棒性问题上面临巨大挑战。针对这些挑战,本文提出了一种基于深度信念网络(DBN)的新型瞬态稳定性预测(TSP)和紧急控制闭环框架。首先,采用分层实时抗抖动 TSP 方法,利用滑动时间窗,同时兼顾准确性和快速性。其次,建立灵敏度回归模型,挖掘功率角与灵敏度之间的隐含关系。当预见到系统即将出现不稳定时,可以及时确定最佳紧急控制策略。最后,将紧急控制后的响应反馈给 TSP 模型。如果预测结果仍不稳定,则将实施额外的控制策略。对新英格兰 IEEE 39 总线系统和东北电力协调委员会(NPCC)140 总线系统进行了综合数值案例研究。结果表明,所提出的方法能尽快检测出系统的不稳定性,并协助维持可靠的系统同步性。
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来源期刊
CiteScore
11.80
自引率
12.70%
发文量
389
审稿时长
26 weeks
期刊介绍: The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.
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