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Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing

Author

Listed:
  • Miftah Al Karim

    (The Lines Company, Te Kuiti 3910, New Zealand)

  • Jonathan Currie

    (Rocket Lab, Auckland 1060, New Zealand)

  • Tek-Tjing Lie

    (School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand)

Abstract

Numerous online methods for post-fault restoration have been tested on different types of systems. Modern power systems are usually operated at design limits and therefore more prone to post-fault instability. However, traditional online methods often struggle to accurately identify events from time series data, as pattern-recognition in a stochastic post-fault dynamic scenario requires fast and accurate fault identification in order to safely restore the system. One of the most prominent methods of pattern-recognition is machine learning. However, machine learning alone is neither sufficient nor accurate enough for making decisions with time series data. This article analyses the application of feature selection to assist a machine learning algorithm to make better decisions in order to restore a multi-machine network which has become islanded due to faults. Within an islanded multi-machine system the number of attributes significantly increases, which makes application of machine learning algorithms even more erroneous. This article contributes by proposing a distributed offline-online architecture. The proposal explores the potential of introducing relevant features from a reduced time series data set, in order to accurately identify dynamic events occurring in different islands simultaneously. The identification of events helps the decision making process more accurate.

Suggested Citation

  • Miftah Al Karim & Jonathan Currie & Tek-Tjing Lie, 2020. "Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing," Energies, MDPI, vol. 13(13), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3494-:d:381071
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    References listed on IDEAS

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    1. Jurado, Sergio & Nebot, Àngela & Mugica, Fransisco & Avellana, Narcís, 2015. "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Elsevier, vol. 86(C), pages 276-291.
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    Cited by:

    1. Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.
    2. Tek-Tjing Lie, 2021. "Editorial to the Special Issue “AI Applications to Power Systems”," Energies, MDPI, vol. 14(18), pages 1-3, September.

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