Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment
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 ne...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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China electric power research institute
2024-01-01
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| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10165678/ |
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| _version_ | 1846113894672629760 |
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| author | Guozheng Wang Jianbo Guo Shicong Ma Kui Luo Xi Zhang Qinglai Guo Shixiong Fan Tiezhu Wang Weilin Hou |
| author_facet | Guozheng Wang Jianbo Guo Shicong Ma Kui Luo Xi Zhang Qinglai Guo Shixiong Fan Tiezhu Wang Weilin Hou |
| author_sort | Guozheng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6c3723ba6b04f418ee72a67d7381fc6 |
| institution | Kabale University |
| issn | 2096-0042 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj-art-c6c3723ba6b04f418ee72a67d7381fc62024-12-21T00:02:19ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422024-01-011062296230710.17775/CSEEJPES.2022.0880010165678Physical Mechanism Enabled Neural Network for Power System Dynamic Security AssessmentGuozheng Wang0Jianbo Guo1Shicong Ma2Kui Luo3Xi Zhang4Qinglai Guo5Shixiong Fan6Tiezhu Wang7Weilin Hou8Beijing Huairou Laboratory,Beijing,ChinaChina Electric Power Research Institute,Department of Power System,Beijing,China,100192China Electric Power Research Institute,Department of Power System,Beijing,China,100192China Electric Power Research Institute,Department of Power System,Beijing,China,100192School of Automation, Beijing Institute of Technology,Beijing,China,100081Tsinghua University,Department of Electrical Engineering,Beijing,China,100084China Electric Power Research Institute,Department of Power System,Beijing,China,100192China Electric Power Research Institute,Department of Power System,Beijing,China,100192China Electric Power Research Institute,Department of Power System,Beijing,China,100192Data-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.https://ieeexplore.ieee.org/document/10165678/Credibility indexmachine intelligenceneural network structurephysical propertiespower systemsecurity assessment |
| spellingShingle | Guozheng Wang Jianbo Guo Shicong Ma Kui Luo Xi Zhang Qinglai Guo Shixiong Fan Tiezhu Wang Weilin Hou Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment CSEE Journal of Power and Energy Systems Credibility index machine intelligence neural network structure physical properties power system security assessment |
| title | Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment |
| title_full | Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment |
| title_fullStr | Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment |
| title_full_unstemmed | Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment |
| title_short | Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment |
| title_sort | physical mechanism enabled neural network for power system dynamic security assessment |
| topic | Credibility index machine intelligence neural network structure physical properties power system security assessment |
| url | https://ieeexplore.ieee.org/document/10165678/ |
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