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A Prediction Method for Galloping of Transmission Lines Based on Improved Neural Network

Received: 7 November 2018     Published: 8 November 2018
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Abstract

The traditional back-propagation neural network (BP) have the disadvantages including the random generation of initial weights and thresholds, easy to fall into the local optimization, and the convergence speed is slow, and it’s hard to confirm the number of neurons in hidden layer. In this paper, the Genetic Algorithm (GA) is utilized to optimize the initial weights and thresholds space of the BP neural network. To obtain the optimal weight matrix and threshold matrix, the error-forward-feedback neural network training is carried out by using the data of transmission line galloping. The trial and error method are used to reduce the number of hidden layer neurons and find the optimal number of neurons. An optimized GA-BP neural network model is established to warn the occurrence of transmission line galloping. The historical data of the transmission lines galloping in the related areas is analyzed by the optimized GA-BP neural network model. The validity and practicability of the proposed GA-BP neural network model is tested and verified. The simulation results show that the GA-BP neural network module could predict the galloping situation of transmission lines more accurately and effectively. As a result, it provides a strong guarantee for preventing large-scale grid fault disasters, and further improves the power grid's ability to withstand natural disasters.

Published in International Journal of Mechanical Engineering and Applications (Volume 6, Issue 4)
DOI 10.11648/j.ijmea.20180604.15
Page(s) 126-133
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Genetic Algorithm, BP Neural Network, Machine Learning Method, Hidden Layer, Transmission Line Galloping

References
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Cite This Article
  • APA Style

    Yongfeng Cheng, Jingshan Han, Bin Liu, Danyu Li. (2018). A Prediction Method for Galloping of Transmission Lines Based on Improved Neural Network. International Journal of Mechanical Engineering and Applications, 6(4), 126-133. https://doi.org/10.11648/j.ijmea.20180604.15

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    ACS Style

    Yongfeng Cheng; Jingshan Han; Bin Liu; Danyu Li. A Prediction Method for Galloping of Transmission Lines Based on Improved Neural Network. Int. J. Mech. Eng. Appl. 2018, 6(4), 126-133. doi: 10.11648/j.ijmea.20180604.15

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    AMA Style

    Yongfeng Cheng, Jingshan Han, Bin Liu, Danyu Li. A Prediction Method for Galloping of Transmission Lines Based on Improved Neural Network. Int J Mech Eng Appl. 2018;6(4):126-133. doi: 10.11648/j.ijmea.20180604.15

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  • @article{10.11648/j.ijmea.20180604.15,
      author = {Yongfeng Cheng and Jingshan Han and Bin Liu and Danyu Li},
      title = {A Prediction Method for Galloping of Transmission Lines Based on Improved Neural Network},
      journal = {International Journal of Mechanical Engineering and Applications},
      volume = {6},
      number = {4},
      pages = {126-133},
      doi = {10.11648/j.ijmea.20180604.15},
      url = {https://doi.org/10.11648/j.ijmea.20180604.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20180604.15},
      abstract = {The traditional back-propagation neural network (BP) have the disadvantages including the random generation of initial weights and thresholds, easy to fall into the local optimization, and the convergence speed is slow, and it’s hard to confirm the number of neurons in hidden layer. In this paper, the Genetic Algorithm (GA) is utilized to optimize the initial weights and thresholds space of the BP neural network. To obtain the optimal weight matrix and threshold matrix, the error-forward-feedback neural network training is carried out by using the data of transmission line galloping. The trial and error method are used to reduce the number of hidden layer neurons and find the optimal number of neurons. An optimized GA-BP neural network model is established to warn the occurrence of transmission line galloping. The historical data of the transmission lines galloping in the related areas is analyzed by the optimized GA-BP neural network model. The validity and practicability of the proposed GA-BP neural network model is tested and verified. The simulation results show that the GA-BP neural network module could predict the galloping situation of transmission lines more accurately and effectively. As a result, it provides a strong guarantee for preventing large-scale grid fault disasters, and further improves the power grid's ability to withstand natural disasters.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - A Prediction Method for Galloping of Transmission Lines Based on Improved Neural Network
    AU  - Yongfeng Cheng
    AU  - Jingshan Han
    AU  - Bin Liu
    AU  - Danyu Li
    Y1  - 2018/11/08
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijmea.20180604.15
    DO  - 10.11648/j.ijmea.20180604.15
    T2  - International Journal of Mechanical Engineering and Applications
    JF  - International Journal of Mechanical Engineering and Applications
    JO  - International Journal of Mechanical Engineering and Applications
    SP  - 126
    EP  - 133
    PB  - Science Publishing Group
    SN  - 2330-0248
    UR  - https://doi.org/10.11648/j.ijmea.20180604.15
    AB  - The traditional back-propagation neural network (BP) have the disadvantages including the random generation of initial weights and thresholds, easy to fall into the local optimization, and the convergence speed is slow, and it’s hard to confirm the number of neurons in hidden layer. In this paper, the Genetic Algorithm (GA) is utilized to optimize the initial weights and thresholds space of the BP neural network. To obtain the optimal weight matrix and threshold matrix, the error-forward-feedback neural network training is carried out by using the data of transmission line galloping. The trial and error method are used to reduce the number of hidden layer neurons and find the optimal number of neurons. An optimized GA-BP neural network model is established to warn the occurrence of transmission line galloping. The historical data of the transmission lines galloping in the related areas is analyzed by the optimized GA-BP neural network model. The validity and practicability of the proposed GA-BP neural network model is tested and verified. The simulation results show that the GA-BP neural network module could predict the galloping situation of transmission lines more accurately and effectively. As a result, it provides a strong guarantee for preventing large-scale grid fault disasters, and further improves the power grid's ability to withstand natural disasters.
    VL  - 6
    IS  - 4
    ER  - 

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Author Information
  • China Electric Power Research Institute, Beijing, China

  • China Electric Power Research Institute, Beijing, China

  • China Electric Power Research Institute, Beijing, China

  • China Electric Power Research Institute, Beijing, China

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