Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (5): 64-74.doi: 10.16088/j.issn.1001-6600.2024071802

• Physics and Electronic Engineering • Previous Articles     Next Articles

Transient Stability Preventive Control Method Based on Deep Extreme Learning Machine

LIU Songkai1,2, ZENG Yucong1,2, ZHANG Lei1,2*, LI Yanzhang3, WANG Qiujie1,2, LIU Longcheng1,2, CHEN Ping1,2, ZHAO Wenbo1,2   

  1. 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang Hubei 443002, China;
    2. Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang Hubei 443002, China;
    3. Wuhan Power Supply Company of State Grid Hubei Electric Power Co., Ltd, Wuhan Hubei 430010, China
  • Received:2024-07-18 Revised:2024-11-04 Online:2025-09-05 Published:2025-08-05

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Abstract: In the transient stability prevention and control of power system, the time domain simulation calculation is complex, and there is a problem of sample class imbalance in the system, which affects the performance of machine learning model. To solve these problems, a transient stability prevention and control method based on deep extreme learning machine (DELM) is proposed. Firstly, the oversampling technique is used to deal with the unbalance of sample class. Then, DELM is used to discover the potential information of the balanced data set, and a mapping model between the operating parameters of the power system and the transient stability index is established. The transient stability prediction model based on DELM is introduced in preventive control to replace the transient stability constraint optimal power flow (TSCOPF) model containing differential algebraic equations with transient stability constraints, the computational complexity is reduced, and the model is solved by firefly algorithm to obtain the final strategy. Finally, the IEEE 39-node system is simulated and verified. The results show that the proposed preventive control method can improve the transient stability of the system at an optimal adjustment cost of \$2 042, adjust the transient instability to stability, and the calculation time of the firefly algorithm can be controlled within 20 s. This indicates that the transient stability prevention and control method based on DELM proposed in this paper can effectively improve the transient stability of the system, and has a fast calculation speed and good economy.

Key words: transient stability, preventive control, optimal power flow, sample imbalance, deep extreme learning machine, firefly algorithm

CLC Number:  TM712;TP181
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