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Online Recognition Method for Voltage Sags Based on a Deep Belief Network

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Listed:
  • Fei Mei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Yong Ren

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Qingliang Wu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Chenyu Zhang

    (State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211113, China)

  • Yi Pan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Haoyuan Sha

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jianyong Zheng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

Suggested Citation

  • Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:43-:d:192952
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    References listed on IDEAS

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    Cited by:

    1. Indu Sekhar Samanta & Subhasis Panda & Pravat Kumar Rout & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis," Energies, MDPI, vol. 16(11), pages 1-31, May.
    2. Alexandre Serrano-Fontova & Pablo Casals Torrens & Ricard Bosch, 2019. "Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies," Energies, MDPI, vol. 12(16), pages 1-21, August.

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