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Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

Author

Listed:
  • Ziyu Bai

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

  • Guoqiang Sun

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

  • Haixiang Zang

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

  • Ming Zhang

    (Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China)

  • Peifeng Shen

    (Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China)

  • Yi Liu

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China)

  • Zhinong Wei

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

Abstract

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3258-:d:260523
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    References listed on IDEAS

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    1. Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
    2. Biao Xu & Xianggen Yin & Dali Wu & Shuai Pang & Yikai Wang, 2019. "An Analytic Method for Power System Fault Diagnosis Employing Topology Description," Energies, MDPI, vol. 12(9), pages 1-17, May.
    3. Huizhong Song & Ming Dong & Rongjie Han & Fushuan Wen & Md. Abdus Salam & Xiaogang Chen & Hua Fan & Jian Ye, 2018. "Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information," Energies, MDPI, vol. 11(10), pages 1-13, September.
    4. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    5. Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
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    Cited by:

    1. 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.

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