International Journal of Energy and Power Engineering

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Power Swing Prediction for Out-of-Step Mitigation

Received: 17 November 2014    Accepted: 20 November 2014    Published: 27 December 2014
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Abstract

This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.

DOI 10.11648/j.ijepe.s.2015040201.16
Published in International Journal of Energy and Power Engineering (Volume 4, Issue 2-1, March 2015)

This article belongs to the Special Issue Electrical Power Systems Operation and Planning

Page(s) 63-72
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

Decision Trees, Power Swing, Out-of-Step, Wide Area Protection

References
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Author Information
  • Department of Electrical Engineering, Pan African University of Basic Science and Technology, Nairobi, Kenya

  • Department of Electrical Engineering, Technical University of Mombasa, Mombasa, Kenya

  • Department of Electrical Engineering, Technical University of Mombasa, Mombasa, Kenya

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  • APA Style

    V. Siyoi, S. Kariuki, M. J. Saulo. (2014). Power Swing Prediction for Out-of-Step Mitigation. International Journal of Energy and Power Engineering, 4(2-1), 63-72. https://doi.org/10.11648/j.ijepe.s.2015040201.16

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    ACS Style

    V. Siyoi; S. Kariuki; M. J. Saulo. Power Swing Prediction for Out-of-Step Mitigation. Int. J. Energy Power Eng. 2014, 4(2-1), 63-72. doi: 10.11648/j.ijepe.s.2015040201.16

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    AMA Style

    V. Siyoi, S. Kariuki, M. J. Saulo. Power Swing Prediction for Out-of-Step Mitigation. Int J Energy Power Eng. 2014;4(2-1):63-72. doi: 10.11648/j.ijepe.s.2015040201.16

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  • @article{10.11648/j.ijepe.s.2015040201.16,
      author = {V. Siyoi and S. Kariuki and M. J. Saulo},
      title = {Power Swing Prediction for Out-of-Step Mitigation},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {2-1},
      pages = {63-72},
      doi = {10.11648/j.ijepe.s.2015040201.16},
      url = {https://doi.org/10.11648/j.ijepe.s.2015040201.16},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijepe.s.2015040201.16},
      abstract = {This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.},
     year = {2014}
    }
    

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    T1  - Power Swing Prediction for Out-of-Step Mitigation
    AU  - V. Siyoi
    AU  - S. Kariuki
    AU  - M. J. Saulo
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    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
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    PB  - Science Publishing Group
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    AB  - This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.
    VL  - 4
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