Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment

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外文摘要：Data-driven artificial intelligence technologies have emerged as increasingly fascinating tools for assessing power sys-tem security.However,their inherent mechanism of inexplicabil-ity and unreliability now limits their scalability in power systems.To address this problem,this paper proposes a neural network design method empowered by physical mechanisms for power sys-tem security assessment.It incorporates geometric characteristics of dynamic security regions into the network training process and constructs connections between network structure and system's unstable mode,which can perform security assessment with a neural network efficiently while ensuring physical plausibility.Furthermore,a credibility evaluation mechanism is established to ensure credibility of neural network predictions and reduce misclassifications.Finally,effectiveness of the proposed method is verified on test systems.Methods and considerations in building a neural network with interpretable structures and credible predictions can provide a reference for machine intelligence applied in other industrial systems.

外文关键词：

Credibility indexmachine intelligenceneural network structurephysical propertiespower systemsecurity assessment

作者：

Guozheng Wang、Jianbo Guo、Shicong Ma、Kui Luo、Xi Zhang、Qinglai Guo、Shixiong Fan、Tiezhu Wang、Weilin Hou

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作者单位：

Beijing Huairou Laboratory,Beijing,China,and the Department of Power System,China Electric Power Research Institute,Beijing 100192,China

Department of Power System,China Electric Power Research Institute,Beijing 100192,China

School of Automation,Beijing Institute of Technology,Beijing 100081,China

Department of Electrical Engineering,Tsinghua University,Beijing 100084,China

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出版年：

2024

DOI：

10.17775/CSEEJPES.2022.08800

中国电机工程学会电力与能源系统学报(英文版)

中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI

ISSN：2096-0042

年,卷(期)：2024.10(6)