Transient stability assessment of power system in combination with update mechanism
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(1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; 2.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China; 3.Wuhan Power Supply Company, State Grid Hubei Electric Power Co., Ltd., Wuhan 430013, China; 4.Taiyuan Power Supply Company, State Grid Shanxi Electric Power Co., Ltd., Taiyuan 030000, China)

Clc Number:

TM712

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    Abstract:

    Power system is a time-varying complex system. In recent years, data-driven machine learning method has been widely used in the field of transient stability assessment of power system. However, when the power system is subjected to a large disturbance and the working condition changes, the machine learning model needs to be trained according to the new operating data. Thus, it is difficult to timely respond to transient stability assessment of the system under the new topology structure. To solve this problem, a model update mechanism is proposed in this paper, which updates the model according to different conditions. In addition, an oblique double random forest with multisurface proximal support vector machine (MPSVM) (MPDRF) model is introduced as a classifier to assess the stable state of power system. The simulation test on the New England 10-machine 39-bus system verifies the effectiveness of the proposed method. The results show that the method combined with update mechanism has high assessment performance, compared with the traditional method.

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刘颂凯,龚 潇,杨 超,刘龙成,李彦彰,张 磊,张雅婷.结合更新机制的电力系统暂态稳定评估研究[J].电力科学与技术学报英文版,2025,40(2):1-9. LIU Songkai, GONG Xiao, YANG Chao, LIU Longcheng, LI Yanzhang, ZHANG Lei, ZHANG Yating. Transient stability assessment of power system in combination with update mechanism[J]. Journal of Electric Power Science and Technology,2025,40(2):1-9.

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  • Online: June 06,2025
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