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A novel differentiation sectionalized strengthen planning method for transmission line based on support vector regression

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Abstract

According to the ideas of risk assessment, the theory of support vector regression is introduced to forecast the ice thickness distribution and achieve the ice extreme value of the wire. Combined with the exponential function of failure rate when the line is under stress, the failure rate model of the line is established in the particular icing condition. The ice strengthening standard is developed by the icing thickness of transmission line, which is used to strengthen lines differentially. Especially, the lines that cross different meteorological conditions are strengthened by the method of segmentation. Finally, the method of economic assessment based on the total life cycle cost is improved, and the comprehensive failure rate is used to calculate differentiated “impairment” benefits. Different lines are made differentiated planning through the simulation example. Various economic indicators are comprehensively compared, in that case, economic assessment results of lines under different strengthening schemes are obtained, which proves the effectiveness of the proposed method.

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Acknowledgements

This work is funded by the State Grid Corporation of China, Major Projects on Natural Science Foundation of China (51207114).

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Authors and Affiliations

  1. School of Electric Engineering, Wuhan University, Wuhan, 430072, China

    Jinshan Luo, Dichen Liu, Jun Wu & Qixin Wang

  2. State Power Economic Research Institute, Beijing, 102209, China

    Jinshan Luo

  3. State Grid Hubei Economic Research Institute, Wuhan, 430077, China

    Jing Yan & Hongsheng Zhao

Authors
  1. Jinshan Luo
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  2. Dichen Liu
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  3. Jun Wu
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  4. Jing Yan
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  5. Hongsheng Zhao
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  6. Qixin Wang
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Corresponding author

Correspondence to Jun Wu.

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Luo, J., Liu, D., Wu, J. et al. A novel differentiation sectionalized strengthen planning method for transmission line based on support vector regression. Neural Comput & Applic 31, 4319–4329 (2019). https://doi.org/10.1007/s00521-017-3308-x

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  • DOI: https://doi.org/10.1007/s00521-017-3308-x

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