Emergency Control Strategy for Transient Angle Instability of Power System Based on Improved AlexNet

被引:0
|
作者
Qiang Z. [1 ]
Wu J. [1 ]
Li B. [1 ]
Zhang R. [1 ,2 ]
Qin L. [1 ]
Hao L. [1 ]
机构
[1] School of Electrical Engineering, Beijing Jiaotong University, Beijing
[2] Institute of Science and Technology, China Three Gorges Corporation, Beijing
来源
关键词
deep CNN; emergency control; improved AlexNet; power system; sensitivity; transient angle instability;
D O I
10.13336/j.1003-6520.hve.20210114
中图分类号
学科分类号
摘要
With the increase of new energy penetration, the power grid environment is increasingly complex, and the safe and stable operation of power system is also facing new challenges. In order to satisfy the real-time emergency control after power system transient angle instability, an emergency control strategy based on improved AlexNet is proposed by combining deep learning with emergency control. Firstly, the power angle trajectories of unstable generators are predicted based on the improved AlexNet to identify critical generators. Then, the sensitivity of emergency control action is defined, and the improved AlexNet sensitivity prediction model is established to fit the mapping relationship between power angle characteristics and the sensitivity, so as to determine the action bus of emergency control. Finally, the emergency control optimization model is established and the strategy is solved with the goal of minimum capacity of the generator tripping and load shedding, and an example is given to verify the model in New England 10 machine 39 bus system. The results show that both the power angle trajectory prediction model and the emergency control sensitivity prediction model based on deep learning have high prediction accuracy. The emergency control strategy formulated on this basis can enable the unstable system to quickly return to stable operation and to strengthen the security and stability defense system of power grid. © 2022 Science Press. All rights reserved.
引用
收藏
页码:2794 / 2804
页数:10
相关论文
共 33 条
  • [1] ZHOU Z Y, XIONG F, HUANG B Y, Et al., Game-theoretical energy management for energy internet with big data-based renewable power forecasting, IEEE Access, 5, pp. 5731-5746, (2017)
  • [2] GU Zhuoyuan, TANG Yong, ZHANG Jian, Et al., Real-time power system transient stability emergency control scheme based on the relative kinetic energy, Proceedings of the CSEE, 34, 7, pp. 1095-1102, (2014)
  • [3] YI Jun, BU Guangquan, GUO Qiang, Et al., Analysis on blackout in Brazilian power grid on March 21, 2018 and its enlightenment to power grid in China, Automation of Electric Power Systems, 43, 2, pp. 1-6, (2019)
  • [4] ZENG Hui, SUN Feng, LI Tie, Et al., Analysis of "9•28" blackout in South Australia and its enlightenment to China, Automation of Electric Power Systems, 41, 13, pp. 1-6, (2017)
  • [5] TENG Lin, LIU Wanshun, YUN Zhihao, Et al., Study of real-time power system transient stability emergency control, Proceedings of the CSEE, 23, 1, pp. 64-69, (2003)
  • [6] FANG D Z, YANG X D, SUN J Q, Et al., An optimal generation rescheduling approach for transient stability enhancement, IEEE Transactions on Power Systems, 22, 1, pp. 386-394, (2007)
  • [7] LE T N, Nguyen N A, QUYEN H A., Emergency control of load shedding based on coordination of artificial neural network and Analytic Hierarchy Process Algorithm, Proceedings of 2017 International Conference on System Science and Engineering (ICSSE), pp. 57-60, (2017)
  • [8] LI Wenyong, Research on transient stability control strategy for multi-machine power system based on EEAC theory, (2017)
  • [9] SHI Z, XU Y, WU X Y, Et al., Coordinated emergency control strategy for transient stability enhancement of AC/DC hybrid power systems based on EEAC theory, Proceedings of the IEEE 2nd International Electrical and Energy Conference (CIEEC), pp. 88-93, (2018)
  • [10] HUANG Xia'nan, Research on power system transient stability assessment and control strategy based on energy transfer function, (2018)