Hybrid state estimation using distributed Compressive Sensing

Reza J. Hamidi; H. Khodabandelou; H. Livani; M. Sami-Fadali

doi:10.1109/PESGM.2016.7742038

Hybrid state estimation using distributed Compressive Sensing

Reza J. Hamidi
, H. Khodabandelou
, H. Livani
, M. Sami-Fadali

Electrical & Computer Engineering

Research output: Contribution to book or proceeding › Conference article › peer-review

8 Scopus citations

Abstract

In this paper, a hybrid state estimation (HSE) method is proposed for the integration of Phasor Measurement Unit (PMU) data into conventional weighted least square state estimators. PMU measurements are not easily compatible with conventional state estimators because PMUs provide different measurement types at a much faster rate than SCADA measurements. However, the vast majority of state estimators are SCADA-based and they cannot utilize PMU data. In the proposed method, PMU data are converted into the SCADA form based on their statistical properties, and the difference between the refreshing rates is compensated using the distributed Compressive Sensing (CS) which exploits the spatial-temporal correlation of PMU data. Simulations are carried out on the IEEE 14- and 57-bus systems to evaluate the proposed hybrid SE. The simulation results are used to discuss the pros and cons of the proposed method.

Original language	English
Title of host publication	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509041688
DOIs	https://doi.org/10.1109/PESGM.2016.7742038
State	Published - Nov 10 2016
Event	2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States Duration: Jul 17 2016 → Jul 21 2016

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2016-November
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/Territory	United States
City	Boston
Period	07/17/16 → 07/21/16

UN SDGs

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

SDG 7 Affordable and Clean Energy

Scopus Subject Areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

Keywords

Compressive sensing
Hybrid state estimation
PMU
SCADA
WLS

Access to Document

10.1109/PESGM.2016.7742038

Cite this

@inproceedings{b2bb8e1bd25242e68f73521a4c2ae996,

title = "Hybrid state estimation using distributed Compressive Sensing",

abstract = "In this paper, a hybrid state estimation (HSE) method is proposed for the integration of Phasor Measurement Unit (PMU) data into conventional weighted least square state estimators. PMU measurements are not easily compatible with conventional state estimators because PMUs provide different measurement types at a much faster rate than SCADA measurements. However, the vast majority of state estimators are SCADA-based and they cannot utilize PMU data. In the proposed method, PMU data are converted into the SCADA form based on their statistical properties, and the difference between the refreshing rates is compensated using the distributed Compressive Sensing (CS) which exploits the spatial-temporal correlation of PMU data. Simulations are carried out on the IEEE 14- and 57-bus systems to evaluate the proposed hybrid SE. The simulation results are used to discuss the pros and cons of the proposed method.",

keywords = "Compressive sensing, Hybrid state estimation, PMU, SCADA, WLS",

author = "Hamidi, \{Reza J.\} and H. Khodabandelou and H. Livani and M. Sami-Fadali",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

year = "2016",

month = nov,

day = "10",

doi = "10.1109/PESGM.2016.7742038",

language = "English",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2016 IEEE Power and Energy Society General Meeting, PESGM 2016",

address = "United States",

}

Hamidi, RJ, Khodabandelou, H, Livani, H & Sami-Fadali, M 2016, Hybrid state estimation using distributed Compressive Sensing. in 2016 IEEE Power and Energy Society General Meeting, PESGM 2016., 7742038, IEEE Power and Energy Society General Meeting, vol. 2016-November, IEEE Computer Society, 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, Boston, United States, 07/17/16. https://doi.org/10.1109/PESGM.2016.7742038

Hybrid state estimation using distributed Compressive Sensing. / Hamidi, Reza J.; Khodabandelou, H.; Livani, H. et al.
2016 IEEE Power and Energy Society General Meeting, PESGM 2016. IEEE Computer Society, 2016. 7742038 (IEEE Power and Energy Society General Meeting; Vol. 2016-November).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Hybrid state estimation using distributed Compressive Sensing

AU - Hamidi, Reza J.

AU - Khodabandelou, H.

AU - Livani, H.

AU - Sami-Fadali, M.

PY - 2016/11/10

Y1 - 2016/11/10

N2 - In this paper, a hybrid state estimation (HSE) method is proposed for the integration of Phasor Measurement Unit (PMU) data into conventional weighted least square state estimators. PMU measurements are not easily compatible with conventional state estimators because PMUs provide different measurement types at a much faster rate than SCADA measurements. However, the vast majority of state estimators are SCADA-based and they cannot utilize PMU data. In the proposed method, PMU data are converted into the SCADA form based on their statistical properties, and the difference between the refreshing rates is compensated using the distributed Compressive Sensing (CS) which exploits the spatial-temporal correlation of PMU data. Simulations are carried out on the IEEE 14- and 57-bus systems to evaluate the proposed hybrid SE. The simulation results are used to discuss the pros and cons of the proposed method.

AB - In this paper, a hybrid state estimation (HSE) method is proposed for the integration of Phasor Measurement Unit (PMU) data into conventional weighted least square state estimators. PMU measurements are not easily compatible with conventional state estimators because PMUs provide different measurement types at a much faster rate than SCADA measurements. However, the vast majority of state estimators are SCADA-based and they cannot utilize PMU data. In the proposed method, PMU data are converted into the SCADA form based on their statistical properties, and the difference between the refreshing rates is compensated using the distributed Compressive Sensing (CS) which exploits the spatial-temporal correlation of PMU data. Simulations are carried out on the IEEE 14- and 57-bus systems to evaluate the proposed hybrid SE. The simulation results are used to discuss the pros and cons of the proposed method.

KW - Compressive sensing

KW - Hybrid state estimation

KW - PMU

KW - SCADA

KW - WLS

UR - https://www.scopus.com/pages/publications/85001555747

U2 - 10.1109/PESGM.2016.7742038

DO - 10.1109/PESGM.2016.7742038

M3 - Conference article

AN - SCOPUS:85001555747

T3 - IEEE Power and Energy Society General Meeting

BT - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

PB - IEEE Computer Society

T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

Y2 - 17 July 2016 through 21 July 2016

ER -

Hybrid state estimation using distributed Compressive Sensing

Abstract

Publication series

Conference

UN SDGs

Scopus Subject Areas

Keywords

Access to Document

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