Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment

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

doi:10.17775/CSEEJPES.2022.08800

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Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment

Regular Papers | 更新时间：2026-10-15

- Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment
- Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第6期页码：2296-2307
- 作者机构：
  
  1. Beijing Huairou Laboratory,Beijing,China
  2. Department of Power System, China Electric Power Research Institute,Beijing,China
  3. School of Automation, Beijing Institute of Technology,Beijing,China
  4. Department of Electrical Engineering, Tsinghua University,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.08800
  中图分类号：
- 纸质出版：2024-11-01
- 稿件说明：
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Guozheng Wang, Jianbo Guo, Shicong Ma, 等. Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(6):2296-2307.

Guozheng Wang, Jianbo Guo, Shicong Ma, et al. Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment[J]. CSEE Journal of Power and Energy Systems, 2024, 10(6): 2296-2307.
Guozheng Wang, Jianbo Guo, Shicong Ma, 等. Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(6):2296-2307. DOI： 10.17775/CSEEJPES.2022.08800.

Guozheng Wang, Jianbo Guo, Shicong Ma, et al. Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment[J]. CSEE Journal of Power and Energy Systems, 2024, 10(6): 2296-2307. DOI： 10.17775/CSEEJPES.2022.08800.

摘要

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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Keywords

references

15. M. Lutter, C. Ritter, and J. Peters, “ Deep lagrangian networks: Using physics as model prior for deep learning ,” 2019, arXiv: 1907.04490. [Online ] . Available: https://arxiv.org/abs/1907.04490.

23. R. Chalapathy, A. K. Menon, and S. Chawla, “ Anomaly detection using one-class neural networks ,” 2018, arXiv: 1802.06360. [Online ] . Available: http://arxiv.org/abs/1802.06360.

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