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Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

Author

Listed:
  • Kai Chen

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

  • Rabea Jamil Mahfoud

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

  • Yonghui Sun

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

  • Dongliang Nan

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
    Electric Power Research Institute, State Grid Electric Power Co., Ltd., Urumqi 830011, China)

  • Kaike Wang

    (Electric Power Research Institute, State Grid Electric Power Co., Ltd., Urumqi 830011, China)

  • Hassan Haes Alhelou

    (Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria)

  • Pierluigi Siano

    (Department of Management & Innovation Systems, University of Salerno, 84084 Salerno, Italy)

Abstract

In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4522-:d:407101
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    References listed on IDEAS

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    1. Jiaying Deng & Wenhai Zhang & Xiaomei Yang, 2019. "Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network," Energies, MDPI, vol. 12(10), pages 1-16, May.
    2. Ziyu Bai & Guoqiang Sun & Haixiang Zang & Ming Zhang & Peifeng Shen & Yi Liu & Zhinong Wei, 2019. "Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China," Energies, MDPI, vol. 12(17), pages 1-19, August.
    3. Alejandro Blanco-M. & Pere Marti-Puig & Karina Gibert & Jordi Cusidó & Jordi Solé-Casals, 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History," Energies, MDPI, vol. 12(10), pages 1-20, May.
    4. Daqian Wei & Bo Wang & Gang Lin & Dichen Liu & Zhaoyang Dong & Hesen Liu & Yilu Liu, 2017. "Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report," Energies, MDPI, vol. 10(3), pages 1-22, March.
    5. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
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    Cited by:

    1. Yan Xu & Mingyu Wang & Wen Fan, 2021. "Defect Data Association Analysis of the Secondary System Based on AFWA-H-Mine," Energies, MDPI, vol. 14(14), pages 1-20, July.
    2. 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.

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