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Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks

Author

Listed:
  • Jiufu Liu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Hongzhong Ma

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Xiaolei Xie

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Jun Cheng

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

Abstract

As the construction of smart grids is in full swing, the number of secondary equipment is also increasing, resulting in an explosive growth of power big data, which is related to the safe and stable operation of power systems. During the operation of the secondary equipment, a large amount of short text data of faults and defects are accumulated, and they are often manually recorded by transportation inspection personnel to complete the classification of defects. Therefore, an automatic text classification based on convolutional neural networks (CNN) is proposed in this paper. Firstly, the topic model is used to mine the global features. At the same time, the word2vec word vector model is used to mine the contextual semantic features of words. Then, the improved LDA topic word vector and word2vec word vector are combined to absorb their respective advantages and utilizations. Finally, the validity and accuracy of the model is verified using actual operational data from the northwest power grid as case study.

Suggested Citation

  • Jiufu Liu & Hongzhong Ma & Xiaolei Xie & Jun Cheng, 2022. "Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks," Energies, MDPI, vol. 15(7), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2400-:d:779058
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    References listed on IDEAS

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    1. Hamza M. Bakr & Mostafa F. Shaaban & Ahmed H. Osman & Hatem F. Sindi, 2020. "Optimal Allocation of Distributed Generation Considering Protection," Energies, MDPI, vol. 13(9), pages 1-18, May.
    2. Kai Chen & Rabea Jamil Mahfoud & Yonghui Sun & Dongliang Nan & Kaike Wang & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM," Energies, MDPI, vol. 13(17), pages 1-17, September.
    3. Guanchen Liu & Peng Zhao & Yang Qin & Mingmin Zhao & Zhichao Yang & Henglin Chen, 2020. "Electromagnetic Immunity Performance of Intelligent Electronic Equipment in Smart Substation’s Electromagnetic Environment," Energies, MDPI, vol. 13(5), pages 1-19, March.
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    Cited by:

    1. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.

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