Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS CSEE Journal of Power and Energy Systems Pub Date : 2023-06-27 DOI:10.17775/CSEEJPES.2022.08800
Guozheng Wang;Jianbo Guo;Shicong Ma;Kui Luo;Xi Zhang;Qinglai Guo;Shixiong Fan;Tiezhu Wang;Weilin Hou
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Abstract

Data-driven artificial intelligence technologies have emerged as increasingly fascinating tools for assessing power system security. However, their inherent mechanism of inexplicability 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 system 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.
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基于物理机制的神经网络电力系统动态安全评估
数据驱动的人工智能技术已经成为评估电力系统安全的越来越有吸引力的工具。然而,其固有的不可解释性和不可靠性限制了其在电力系统中的可扩展性。针对这一问题,本文提出了一种基于物理机制的电力系统安全评估神经网络设计方法。将动态安全区域的几何特征融入到网络训练过程中,构建网络结构与系统不稳定模式之间的联系,在保证物理合理性的前提下,有效地利用神经网络进行安全评估。在此基础上,建立了可信度评价机制,以保证神经网络预测的可信度,减少误分类。最后,在测试系统上验证了该方法的有效性。构建具有可解释结构和可信预测的神经网络的方法和考虑,可以为机器智能在其他工业系统中的应用提供参考。
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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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