Artificial intelligence techniques for stability analysis in modern power systems
Effective stability analysis is essential for the secure operation of modern power systems. As smart grids evolve with increased interconnection, renewable energy integration, and electrification, the large-scale deployment of ultra-high voltage AC/DC networks introduces various operational modes an...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | iEnergy |
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| Online Access: | https://www.sciopen.com/article/10.23919/IEN.2024.0027 |
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| _version_ | 1841550178791718912 |
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| author | Jiashu Fang Chongru Liu |
| author_facet | Jiashu Fang Chongru Liu |
| author_sort | Jiashu Fang |
| collection | DOAJ |
| description | Effective stability analysis is essential for the secure operation of modern power systems. As smart grids evolve with increased interconnection, renewable energy integration, and electrification, the large-scale deployment of ultra-high voltage AC/DC networks introduces various operational modes and potential fault points, posing significant challenges to maintaining stability. Traditional analysis and control methods fall short under these conditions. In contrast, emerging artificial intelligence (AI) techniques, combined with real-time data collection, provide powerful tools for enhancing stability analysis in smart grids. This paper comprehensively explores AI techniques in stability analysis, discussing the necessity and rationale for integrating AI into stability analysis through the lenses of knowledge fusion, discovery, and adaptation. It provides a thorough review of current studies on AI applications in stability analysis, addresses key challenges, and outlines future prospects for AI integration, highlighting its potential to improve analytical capabilities in complex power systems. |
| format | Article |
| id | doaj-art-4527b1db4f324c5a888b66fd47c687e6 |
| institution | Kabale University |
| issn | 2771-9197 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | iEnergy |
| spelling | doaj-art-4527b1db4f324c5a888b66fd47c687e62025-01-10T06:52:43ZengTsinghua University PressiEnergy2771-91972024-12-013419421510.23919/IEN.2024.0027Artificial intelligence techniques for stability analysis in modern power systemsJiashu Fang0Chongru Liu1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaEffective stability analysis is essential for the secure operation of modern power systems. As smart grids evolve with increased interconnection, renewable energy integration, and electrification, the large-scale deployment of ultra-high voltage AC/DC networks introduces various operational modes and potential fault points, posing significant challenges to maintaining stability. Traditional analysis and control methods fall short under these conditions. In contrast, emerging artificial intelligence (AI) techniques, combined with real-time data collection, provide powerful tools for enhancing stability analysis in smart grids. This paper comprehensively explores AI techniques in stability analysis, discussing the necessity and rationale for integrating AI into stability analysis through the lenses of knowledge fusion, discovery, and adaptation. It provides a thorough review of current studies on AI applications in stability analysis, addresses key challenges, and outlines future prospects for AI integration, highlighting its potential to improve analytical capabilities in complex power systems.https://www.sciopen.com/article/10.23919/IEN.2024.0027smart gridsartificial intelligencedeep learningstability analysis |
| spellingShingle | Jiashu Fang Chongru Liu Artificial intelligence techniques for stability analysis in modern power systems iEnergy smart grids artificial intelligence deep learning stability analysis |
| title | Artificial intelligence techniques for stability analysis in modern power systems |
| title_full | Artificial intelligence techniques for stability analysis in modern power systems |
| title_fullStr | Artificial intelligence techniques for stability analysis in modern power systems |
| title_full_unstemmed | Artificial intelligence techniques for stability analysis in modern power systems |
| title_short | Artificial intelligence techniques for stability analysis in modern power systems |
| title_sort | artificial intelligence techniques for stability analysis in modern power systems |
| topic | smart grids artificial intelligence deep learning stability analysis |
| url | https://www.sciopen.com/article/10.23919/IEN.2024.0027 |
| work_keys_str_mv | AT jiashufang artificialintelligencetechniquesforstabilityanalysisinmodernpowersystems AT chongruliu artificialintelligencetechniquesforstabilityanalysisinmodernpowersystems |