Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study

Dahale, Shweta; Karimi, Hazhar Sufi; Lai, Kexing; Natarajan, Balasubramaniam

doi:10.1109/ACCESS.2020.3035378

Title: Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study

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Abstract

The power distribution grid is typically unobservable due to a lack of measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Therefore, developing state estimation methods that enhance situational awareness at the grid edge with compressed measurements is critical. For this purpose, a suite of sparsity-based approaches that exploit the correlation among states/measurements in spatial as well as temporal domains have been proposed recently. This paper presents a systematic comparison and evaluation of these approaches. Specifically, the performance and complexity of spatial methods (1-D compressive sensing and matrix completion) and spatio-temporal methods (2-D compressive sensing and tensor completion) are compared using the IEEE 37 and IEEE 123 bus test systems. Additionally, new robust formulations of these sparsity-based methods are derived and shown to be robust to bad data and network parameter uncertainties. Among the sparsity-based approaches, compressive sensing methods tend to outperform matrix completion and tensor completion methods in terms of error performance.

Authors:

;

Publication Date:: Wed Jan 01 00:00:00 EST 2020

Research Org.:: Kansas State Univ., Manhattan, KS (United States)

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

OSTI Identifier:: 1836323

Alternate Identifier(s):: OSTI ID: 1799538; OSTI ID: 1905303

Grant/Contract Number:: EE0008767

Resource Type:: Published Article

Journal Name:: IEEE Access

Additional Journal Information:: Journal Name: IEEE Access Journal Volume: 8; Journal ID: ISSN 2169-3536

Publisher:: Institute of Electrical and Electronics Engineers

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Computer Science; Engineering; Telecommunications; Bad data; compressive sensing; matrix completion; power distribution; state estimation

Citation Formats


                    Dahale, Shweta, Karimi, Hazhar Sufi, Lai, Kexing, and Natarajan, Balasubramaniam. Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study.  United States: N. p., 2020. 
Web.  doi:10.1109/ACCESS.2020.3035378.

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                    Dahale, Shweta, Karimi, Hazhar Sufi, Lai, Kexing, & Natarajan, Balasubramaniam. Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study.  United States.  https://doi.org/10.1109/ACCESS.2020.3035378

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                    Dahale, Shweta, Karimi, Hazhar Sufi, Lai, Kexing, and Natarajan, Balasubramaniam. Wed .  
"Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study".  United States.  https://doi.org/10.1109/ACCESS.2020.3035378.

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@article{osti_1836323,

  title        = {Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study},

  author       = {Dahale, Shweta and Karimi, Hazhar Sufi and Lai, Kexing and Natarajan, Balasubramaniam},

  abstractNote = {The power distribution grid is typically unobservable due to a lack of measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Therefore, developing state estimation methods that enhance situational awareness at the grid edge with compressed measurements is critical. For this purpose, a suite of sparsity-based approaches that exploit the correlation among states/measurements in spatial as well as temporal domains have been proposed recently. This paper presents a systematic comparison and evaluation of these approaches. Specifically, the performance and complexity of spatial methods (1-D compressive sensing and matrix completion) and spatio-temporal methods (2-D compressive sensing and tensor completion) are compared using the IEEE 37 and IEEE 123 bus test systems. Additionally, new robust formulations of these sparsity-based methods are derived and shown to be robust to bad data and network parameter uncertainties. Among the sparsity-based approaches, compressive sensing methods tend to outperform matrix completion and tensor completion methods in terms of error performance.},

  doi          = {10.1109/ACCESS.2020.3035378},

  journal      = {IEEE Access},

  number       = ,

  volume       = 8,

  place        = {United States},

  year         = {Wed Jan 01 00:00:00 EST 2020},

  month        = {Wed Jan 01 00:00:00 EST 2020}

}

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