Comparison of Gaussian Mixture Reductions for Probabilistic Studies in Power Systems

Jairo Quiros Tortos

Comparison of Gaussian Mixture Reductions for Probabilistic Studies in Power Systems

Jairo Quiros Tortos

Research output: Chapter in Book/Conference proceeding › Conference 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 language	English
Title of host publication	host publication
Publisher	IEEE
Number of pages	7
Publication status	Published - Jul 2012
Event	IEEE PES General Meeting 2012 - Duration: 22 Jul 2012 → 26 Jul 2012

Conference

Conference	IEEE PES General Meeting 2012
Period	22/07/12 → 26/07/12

Keywords

Gaussian mixture model
probabilistic density function
probabilistic power flow
wind power

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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@inproceedings{0886fbde91a74ecf920ac31ce6084a40,

title = "Comparison of Gaussian Mixture Reductions for Probabilistic Studies in Power Systems",

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.",

keywords = "Gaussian mixture model, probabilistic density function, probabilistic power flow, wind power",

author = "{Quiros Tortos}, Jairo",

year = "2012",

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N2 - 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.

AB - 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.

KW - Gaussian mixture model

KW - probabilistic density function

KW - probabilistic power flow

KW - wind power

M3 - Conference contribution

BT - host publication

PB - IEEE

T2 - IEEE PES General Meeting 2012

Y2 - 22 July 2012 through 26 July 2012

ER -

Comparison of Gaussian Mixture Reductions for Probabilistic Studies in Power Systems

Abstract

Conference

Keywords

UN SDGs

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