Estimation of missing transmission line reactance data using multiple linear regression

Anton C.Sameepa Hettiarachchige-Don; Visvakumar Aravinthan

doi:10.1109/NAPS.2017.8107278

Estimation of missing transmission line reactance data using multiple linear regression

Anton C.Sameepa Hettiarachchige-Don, Visvakumar Aravinthan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper explores the use of Multiple Linear Regression techniques in order to estimate sections of missing line reactance data sometimes found in the data received from synchrophasor measurement units. The high correlation between transmission line reactance and the system frequency is used to predict these estimates. Dynamic predictor coefficients are used to improve accuracy of the estimations and analysis is done to determine the most appropriate parameters to use in the regression model. All model building, analysis and testing is done using multiple sections of real PMU data.

Original language	English
Title of host publication	2017 North American Power Symposium, NAPS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781538626993
ISBN (Print)	9781538627006
DOIs	https://doi.org/10.1109/NAPS.2017.8107278
Publication status	Published - 16 Nov 2017
Event	2017 North American Power Symposium, NAPS 2017 - Morgantown, United States Duration: 17 Sept 2017 → 19 Sept 2017

Conference

Conference	2017 North American Power Symposium, NAPS 2017
Country/Territory	United States
City	Morgantown
Period	17/09/17 → 19/09/17

Keywords

Estimation
Multiple Linear Regression
PMU
Smart Grid
Synchrophasor Measurement Units

UN SDGs

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

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10.1109/NAPS.2017.8107278

Cite this

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title = "Estimation of missing transmission line reactance data using multiple linear regression",

abstract = "This paper explores the use of Multiple Linear Regression techniques in order to estimate sections of missing line reactance data sometimes found in the data received from synchrophasor measurement units. The high correlation between transmission line reactance and the system frequency is used to predict these estimates. Dynamic predictor coefficients are used to improve accuracy of the estimations and analysis is done to determine the most appropriate parameters to use in the regression model. All model building, analysis and testing is done using multiple sections of real PMU data.",

keywords = "Estimation, Multiple Linear Regression, PMU, Smart Grid, Synchrophasor Measurement Units",

author = "Hettiarachchige-Don, {Anton C.Sameepa} and Visvakumar Aravinthan",

note = "No ISSN; 2017 North American Power Symposium, NAPS 2017 ; Conference date: 17-09-2017 Through 19-09-2017",

year = "2017",

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doi = "10.1109/NAPS.2017.8107278",

language = "English",

isbn = "9781538627006",

booktitle = "2017 North American Power Symposium, NAPS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Hettiarachchige-Don, Anton C.Sameepa

AU - Aravinthan, Visvakumar

N1 - No ISSN

PY - 2017/11/16

Y1 - 2017/11/16

N2 - This paper explores the use of Multiple Linear Regression techniques in order to estimate sections of missing line reactance data sometimes found in the data received from synchrophasor measurement units. The high correlation between transmission line reactance and the system frequency is used to predict these estimates. Dynamic predictor coefficients are used to improve accuracy of the estimations and analysis is done to determine the most appropriate parameters to use in the regression model. All model building, analysis and testing is done using multiple sections of real PMU data.

AB - This paper explores the use of Multiple Linear Regression techniques in order to estimate sections of missing line reactance data sometimes found in the data received from synchrophasor measurement units. The high correlation between transmission line reactance and the system frequency is used to predict these estimates. Dynamic predictor coefficients are used to improve accuracy of the estimations and analysis is done to determine the most appropriate parameters to use in the regression model. All model building, analysis and testing is done using multiple sections of real PMU data.

KW - Estimation

KW - Multiple Linear Regression

KW - PMU

KW - Smart Grid

KW - Synchrophasor Measurement Units

UR - http://www.scopus.com/inward/record.url?scp=85040631542&partnerID=8YFLogxK

U2 - 10.1109/NAPS.2017.8107278

DO - 10.1109/NAPS.2017.8107278

M3 - Conference contribution

AN - SCOPUS:85040631542

SN - 9781538627006

BT - 2017 North American Power Symposium, NAPS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 North American Power Symposium, NAPS 2017

Y2 - 17 September 2017 through 19 September 2017

ER -

Estimation of missing transmission line reactance data using multiple linear regression

Abstract

Conference

Keywords

UN SDGs

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