Estimation of Transmission Line Sequence Impedances using Real PMU Data

Prashanth Kumar Mansani, Anamitra Pal, Matthew Rhodes, Brian Keel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Accurate knowledge of transmission line parameters in general, and sequence impedances, in particular, plays an important role in state estimation, fault detection, and adjustment of relay settings. Line parameter estimation using online methods has attracted considerable interest with the widespread installation of phasor measurement units (PMUs). Although various methods have been proposed in the literature for line parameter estimation, most of them have been tested on purely synthetic datasets. A synthetic dataset does not capture the nuances of real data, such as measurement invariance and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. In this paper, a three-stage test procedure is developed to compare the performance of two algorithms, namely, moving-window total least squares (MWTLS) recursive Kalman filter (RKF), on real PMU data. The results prove that RKF is better than MWTLS. This paper also proposes using ASPEN data as an initial estimate to RKF for further improving its performance. Finally, to circumvent the problems faced due to data dropouts, an auto regressive integrated moving average (ARIMA) model is implemented to predict the variations in sequence impedances.

Original languageEnglish (US)
Title of host publication2018 North American Power Symposium, NAPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538671382
DOIs
StatePublished - Jan 2 2019
Event2018 North American Power Symposium, NAPS 2018 - Fargo, United States
Duration: Sep 9 2018Sep 11 2018

Publication series

Name2018 North American Power Symposium, NAPS 2018

Conference

Conference2018 North American Power Symposium, NAPS 2018
CountryUnited States
CityFargo
Period9/9/189/11/18

Keywords

  • ARIMA
  • ASPEN
  • Parameter estimation
  • Phasor measurement unit (PMU)
  • Recursive Kalman filter (RKF)
  • Total least squares (TLS)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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