Comparison of Gaussian Mixture Reductions for Probabilistic Studies in Power Systems

Jairo Quiros Tortos

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Abstract

    This paper presents the comparison of three pair-merging methods to reduce the number of Gaussian mixture components used to model non-Gaussian Probabilistic Density Function (PDF) of random power system variables such as power demands, wind power outputs or other intermittent power sources. It also introduces a fine-tuning algorithm to improve the solution of the pair-merging methods to better approximate the original Gaussian mixture. A Gaussian mixture distribution with seven components is used to validate and demonstrate the algorithms.
    Original languageEnglish
    Title of host publicationhost publication
    PublisherIEEE
    Number of pages7
    Publication statusPublished - Jul 2012
    EventIEEE PES General Meeting 2012 -
    Duration: 22 Jul 201226 Jul 2012

    Conference

    ConferenceIEEE PES General Meeting 2012
    Period22/07/1226/07/12

    Keywords

    • Gaussian mixture model
    • probabilistic density function
    • probabilistic power flow
    • wind power

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