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Transient Stability Feature Selection Method Based on Deep Learning Technology

  • Wei Ru WangEmail author
  • Xin Cong Shi
  • Xue Ting Cheng
  • Jin Hao Wang
  • Xin Yuan Liu
  • Jie Hao
Conference paper
  • 37 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1060)

Abstract

As a key link in the transient stability assessment of power system, feature selection is the basis to ensure the transient stability assessment results. In view of the defects in the methods proposed in the existing literatures at home and abroad, this paper proposes a deep learning model adapted to the feature extraction of power grid simulation data, which is based on the deep topology convolutional network to extract features. Simulation results show that the proposed model has very high reliability for network stability, and the obtained characteristic quantity can be effectively connected with the data analysis algorithm and achieve good results.

Keywords

Deep learning Graph convolution network (GCN) Transient stability assessment (TSA) Fast stability determination 

References

  1. 1.
    Zhang C, Li Y, Yu Z, et al. A weighted random forest approach to improve predictive performance for power system transient stability assessment. In: 2016 power and energy engineering conference (APPEEC). Xi’an: IEEE; 2016. p. 1259–63.Google Scholar
  2. 2.
    Shengyong Y, et al. Study on power systems transient stability assessment based on machine learning method. Chengdu: Southwest Jiaotong University; 2010.Google Scholar
  3. 3.
    Tso SK, Gu XP. Feature selection by separability assessment of input spaces for transient stability classification based on neural networks. Int J Electr Power Energy Syst. 2004;26(3):153–62.CrossRefGoogle Scholar
  4. 4.
    Sawhney H, Jeyasurya B. A feed-forward artificial neural network with enhanced feature selection for power system transient stability assessment. Electr Power Syst Res. 2006;76(12):1047–54.CrossRefGoogle Scholar
  5. 5.
    Qian M, Yihan Y, Wenying L, et al. Power system transient stability assessment with combined SVM method mixing multiple input features. Proc CSEE. 2005;25(6):17–23.Google Scholar
  6. 6.
    Ye S, Wang X, Liu Z, et al. Transient stability assessment based on random forest algorithm. J Southwest Jiaotong Univ. 2008;43(5):573–7.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Wei Ru Wang
    • 1
    Email author
  • Xin Cong Shi
    • 2
  • Xue Ting Cheng
    • 1
  • Jin Hao Wang
    • 1
  • Xin Yuan Liu
    • 1
  • Jie Hao
    • 1
  1. 1.State Grid Shanxi Electric Power Research InstituteTaiyuanChina
  2. 2.State Grid Shanxi Electric Power Company Lingchuan Power Supply Company, LingchuanJinchengChina

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