A robust prony method for power system electromechanical modes identification

Marcos Netto, Lamine Mili

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

26 Scopus citations

Abstract

This paper presents a robust parametric estimation method of the Prony exponential model that is able to suppress white impulsive noise. The method consists of the following steps. Firstly, the Prony parametric estimation problem is reformulated as a parameter estimation of an Auto-Regressive (AR) model of a known order. Secondly, the outliers of the complex-valued data samples, which are induced by impulsive noise, are identified and suppressed using the iteratively reweighted phase-phase correlator (IPPC); the latter is a robust estimator of correlation for complex-valued Gaussian processes, which has been extended here to handle outliers in the magnitude and in the phase angle of voltage phasor measurements. Finally, the Burg algorithm is applied using a robustly estimated autocorrelation sequence to estimate the AR parameters. The Burg algorithm is chosen over the Yule-Walker technique because it leads to stable AR models even when the processed data samples are of short durations and when the roots of the characteristic polynomial are close to the unit circle, which is precisely the case for power systems with poorly damped excited modes. The good performance of the proposed method is demonstrated on some simulations carried out on the two-area test system. The method is very fast to compute and compatible with real-time application requirements.

Original languageEnglish (US)
Title of host publication2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PublisherIEEE Computer Society
Pages1-5
Number of pages5
ISBN (Electronic)9781538622124
DOIs
StatePublished - Jan 29 2018
Externally publishedYes
Event2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States
Duration: Jul 16 2017Jul 20 2017

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2018-January
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Country/TerritoryUnited States
CityChicago
Period7/16/177/20/17

All Science Journal Classification (ASJC) codes

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

Keywords

  • Autoregressive parameter estimation
  • Electromechanical modes of oscillation
  • Modal analysis
  • Phase-phase correlator
  • Robust Prony method
  • Small-signal stability
  • Spectral analysis

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