American Journal of Electrical Power and Energy Systems

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Probabilistic Voltage Stability Analysis Based on Unscented Transformation and Maximum Entropy Principle

Received: 11 November 2016    Accepted: 28 November 2016    Published: 06 January 2017
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Abstract

Considering the uncertainty of load and the random variation of wind farm output power in power system, a probabilistic voltage stability analysis method is proposed based on unscented transformation technique. According to the statistical characteristics of random variables in power system, the statistical characteristics of voltage stability margins, such as mean, standard deviation and moments, can be calculated by using a small number of samples and the conventional method. The maximum entropy method is applied to determine the probability distribution of voltage stability margin. In compared with Monte Carlo method, the effectiveness of the proposed method is verified on 39-bus and IEEE 57-bus system. The results show that the proposed method can accurately compute the statistical characteristics and the probability distribution of the voltage stability margin, and the computational efficiency is improved.

DOI 10.11648/j.epes.20160506.14
Published in American Journal of Electrical Power and Energy Systems (Volume 5, Issue 6, November 2016)
Page(s) 81-90
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), 2024. Published by Science Publishing Group

Keywords

Voltage Stability, Probabilistic Voltage Stability Margin, Unscented Transformation Technique, Sigma Points, Maximum Entropy Principle

References
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Author Information
  • Electrical and Information College of Jinan University, Zhuhai, China

  • IEEE (HK) PES/IAS/PELS/IES Joint Chapter, Hong Kong, China

  • State Grid East China Control Center, Shanghai, China

  • School of Electrical Engineering, Zhengzhou University, Zhengzhou, China

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  • APA Style

    Zhang Jianfen, Tse Chitong, Liu Yi, Wang Kewen. (2017). Probabilistic Voltage Stability Analysis Based on Unscented Transformation and Maximum Entropy Principle. American Journal of Electrical Power and Energy Systems, 5(6), 81-90. https://doi.org/10.11648/j.epes.20160506.14

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    ACS Style

    Zhang Jianfen; Tse Chitong; Liu Yi; Wang Kewen. Probabilistic Voltage Stability Analysis Based on Unscented Transformation and Maximum Entropy Principle. Am. J. Electr. Power Energy Syst. 2017, 5(6), 81-90. doi: 10.11648/j.epes.20160506.14

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    AMA Style

    Zhang Jianfen, Tse Chitong, Liu Yi, Wang Kewen. Probabilistic Voltage Stability Analysis Based on Unscented Transformation and Maximum Entropy Principle. Am J Electr Power Energy Syst. 2017;5(6):81-90. doi: 10.11648/j.epes.20160506.14

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  • @article{10.11648/j.epes.20160506.14,
      author = {Zhang Jianfen and Tse Chitong and Liu Yi and Wang Kewen},
      title = {Probabilistic Voltage Stability Analysis Based on Unscented Transformation and Maximum Entropy Principle},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {5},
      number = {6},
      pages = {81-90},
      doi = {10.11648/j.epes.20160506.14},
      url = {https://doi.org/10.11648/j.epes.20160506.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.epes.20160506.14},
      abstract = {Considering the uncertainty of load and the random variation of wind farm output power in power system, a probabilistic voltage stability analysis method is proposed based on unscented transformation technique. According to the statistical characteristics of random variables in power system, the statistical characteristics of voltage stability margins, such as mean, standard deviation and moments, can be calculated by using a small number of samples and the conventional method. The maximum entropy method is applied to determine the probability distribution of voltage stability margin. In compared with Monte Carlo method, the effectiveness of the proposed method is verified on 39-bus and IEEE 57-bus system. The results show that the proposed method can accurately compute the statistical characteristics and the probability distribution of the voltage stability margin, and the computational efficiency is improved.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Probabilistic Voltage Stability Analysis Based on Unscented Transformation and Maximum Entropy Principle
    AU  - Zhang Jianfen
    AU  - Tse Chitong
    AU  - Liu Yi
    AU  - Wang Kewen
    Y1  - 2017/01/06
    PY  - 2017
    N1  - https://doi.org/10.11648/j.epes.20160506.14
    DO  - 10.11648/j.epes.20160506.14
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 81
    EP  - 90
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20160506.14
    AB  - Considering the uncertainty of load and the random variation of wind farm output power in power system, a probabilistic voltage stability analysis method is proposed based on unscented transformation technique. According to the statistical characteristics of random variables in power system, the statistical characteristics of voltage stability margins, such as mean, standard deviation and moments, can be calculated by using a small number of samples and the conventional method. The maximum entropy method is applied to determine the probability distribution of voltage stability margin. In compared with Monte Carlo method, the effectiveness of the proposed method is verified on 39-bus and IEEE 57-bus system. The results show that the proposed method can accurately compute the statistical characteristics and the probability distribution of the voltage stability margin, and the computational efficiency is improved.
    VL  - 5
    IS  - 6
    ER  - 

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