Intelligent Evolution of Power System Stability Analysis Techniques: From Model-Driven, Data-Driven to Hybrid Intelligence

Junbo ZHANG, Xianghui XIAO, Yu MA, Ying PENG, Qingyuan ZHOU, Rui LI, Kangjie HE

South Power Sys Technol ›› 2025, Vol. 19 ›› Issue (7) : 30-49.

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South Power Sys Technol ›› 2025, Vol. 19 ›› Issue (7) : 30-49. DOI: 10.13648/j.cnki.issn1674-0629.2025.07.003
Special Column on Boao New Power System International Forum

Intelligent Evolution of Power System Stability Analysis Techniques: From Model-Driven, Data-Driven to Hybrid Intelligence

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Abstract

With the increasing complexity of "high-proportion renewable and high-electrification" power systems, traditional model-driven stability analysis methods face growing limitations. Artificial intelligence (AI) techniques, offering advantages in both speed and accuracy, have emerged as a key research focus. This paper focuses on the development path of power system stability analysis technology from model driven to data-driven, and further to hybrid intelligence integrating domain knowledge and data. Firstly, the technical requirements of typical stability analysis tasks are outlined, and traditional model driven methods are reviewed.Subsequently, the application results of artificial intelligence methods in different scenarios are summarized, and their advantages and disadvantages are analyzed. Further the information driven methods and research progress of integrating power system knowledge and mechanisms are explored. Finally, based on the power system characteristics under the background of "double high", the current challenges faced are analyzed, and future research directions are discussed.

Key words

power system stability / artificial intelligence / machine learning / data-driven / knowledge-data-combined method

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Junbo ZHANG , Xianghui XIAO , Yu MA , et al . Intelligent Evolution of Power System Stability Analysis Techniques: From Model-Driven, Data-Driven to Hybrid Intelligence[J]. Southern Power System Technology. 2025, 19(7): 30-49 https://doi.org/10.13648/j.cnki.issn1674-0629.2025.07.003

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Funding

the National Natural Science Foundation of China(52277101)
the Key Technology and Research Project of Power Construction Corporation of China, Co., Ltd(DJ-HXGG-2024-01)
the Central University Basic Research Funds for South Chnia University of Technology(2024ZYGXZR109)
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