Distributed Learning of Mode Shapes in Power System Models

Gusrialdi, Azwirman; Chakrabortty, Aranya; Qu, Zhihua

doi:10.1109/cdc.2018.8618949

Title: Distributed Learning of Mode Shapes in Power System Models

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Abstract

We address the problem of distributed estimation of eigenvectors for power system models using online phasor measurements. The power system is considered to be divided into a set of non-overlapping areas, each of which is equipped with a local estimator. Online measurements of bus voltage and current phasors are first used to generate estimates of the generator states in each area using decentralized Kalman filters. Thereafter, these estimates are used for identifying a reduced-order model of the system in a completely distributed way by sharing state information between the estimators over a strongly connected communication graph. The identified model is then utilized to estimate its right eigenvectors over the same distributed graph. Results are validated using a 50-bus power system model with four areas.

Authors:: Gusrialdi, Azwirman; Chakrabortty, Aranya; Qu, Zhihua

Publication Date:: Sat Dec 01 00:00:00 EST 2018

Research Org.:: Univ. of Central Florida, Orlando, FL (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1826256

Report Number(s):: DOE-UCF-7998

DOE Contract Number:: EE0007998

Resource Type:: Conference

Journal Name:: 2018 IEEE Conference on Decision and Control (CDC)

Additional Journal Information:: Conference: 2018 IEEE Conference on Decision and Control (CDC)

Country of Publication:: United States

Language:: English

Citation Formats


                    Gusrialdi, Azwirman, Chakrabortty, Aranya, and Qu, Zhihua. Distributed Learning of Mode Shapes in Power System Models.  United States: N. p., 2018. 
        Web.  doi:10.1109/cdc.2018.8618949.

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                    Gusrialdi, Azwirman, Chakrabortty, Aranya, & Qu, Zhihua. Distributed Learning of Mode Shapes in Power System Models.  United States.  https://doi.org/10.1109/cdc.2018.8618949

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                    Gusrialdi, Azwirman, Chakrabortty, Aranya, and Qu, Zhihua. 2018.  
        "Distributed Learning of Mode Shapes in Power System Models".  United States.  https://doi.org/10.1109/cdc.2018.8618949.  https://www.osti.gov/servlets/purl/1826256.

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@article{osti_1826256,

  title        = {Distributed Learning of Mode Shapes in Power System Models},

  author       = {Gusrialdi, Azwirman and Chakrabortty, Aranya and Qu, Zhihua},

  abstractNote = {We address the problem of distributed estimation of eigenvectors for power system models using online phasor measurements. The power system is considered to be divided into a set of non-overlapping areas, each of which is equipped with a local estimator. Online measurements of bus voltage and current phasors are first used to generate estimates of the generator states in each area using decentralized Kalman filters. Thereafter, these estimates are used for identifying a reduced-order model of the system in a completely distributed way by sharing state information between the estimators over a strongly connected communication graph. The identified model is then utilized to estimate its right eigenvectors over the same distributed graph. Results are validated using a 50-bus power system model with four areas.},

  doi          = {10.1109/cdc.2018.8618949},

  url          = {https://www.osti.gov/biblio/1826256},
  journal      = {2018 IEEE Conference on Decision and Control (CDC)},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Dec 01 00:00:00 EST 2018},

  month        = {Sat Dec 01 00:00:00 EST 2018}

}

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