Online Damping Ratio Prediction Using Locally Weighted Linear Regression

Junbo Zhang, C. Y. Chung, Yingduo Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)

Abstract

In this study, a locally weighted linear regression (LWLR) method is proposed to predict damping ratio of a dominant mode online. The LWLR method, which is nonparametric and data-oriented, is essentially proposed for nonlinear data fitting; therefore, it can track the nonlinear power system operations and help damping ratio prediction in real power systems, which is hardly achieved by the conventional linear regression. To successfully implement this method, the measurement of weighting value and the choice of weighting function as well as its parameter setting, related to prediction accuracy and numerical conditions, are extensively discussed. Simulations are carried out in a two-area four-machine system and a large complex system, China Southern Grid. Both results validate the effectiveness of the proposed method.

Original languageEnglish
Article number7155603
Pages (from-to)1954-1962
Number of pages9
JournalIEEE Transactions on Power Systems
Volume31
Issue number3
DOIs
Publication statusPublished - May 2016
Externally publishedYes

Keywords

  • Locally weighted linear regression (LWLR)
  • mode estimation
  • online damping ratio prediction
  • wide-area measurement system (WAMS)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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