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Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

Author

Listed:
  • Guoqiang Sun

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

  • Xiaoliu Ding

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

  • Zhinong Wei

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

  • Peifeng Shen

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

  • Yang Zhao

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

  • Qiugen Huang

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

  • Liang Zhang

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

  • Haixiang Zang

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

Abstract

Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages.

Suggested Citation

  • Guoqiang Sun & Xiaoliu Ding & Zhinong Wei & Peifeng Shen & Yang Zhao & Qiugen Huang & Liang Zhang & Haixiang Zang, 2019. "Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory," Energies, MDPI, vol. 12(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2814-:d:250547
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    References listed on IDEAS

    as
    1. Yuanbing Zheng & Caixin Sun & Jian Li & Qing Yang & Weigen Chen, 2011. "Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data," Energies, MDPI, vol. 4(8), pages 1-10, August.
    2. Lu Gan & Dirong Xu & Lin Hu & Lei Wang, 2017. "Economic Feasibility Analysis for Renewable Energy Project Using an Integrated TFN–AHP–DEA Approach on the Basis of Consumer Utility," Energies, MDPI, vol. 10(12), pages 1-21, December.
    3. 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.
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