Robust Koopman Operator-based Kalman Filter for Power Systems Dynamic State Estimation

Marcos Netto, Lamine Mili

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

8 Scopus citations

Abstract

This paper develops a robust generalized maximum-likelihood Koopman operator-based Kalman filter (GM-KKF) to estimate the rotor angle of synchronous generators. The approach is data-driven and model independent. The design phase is carried out offline and requires estimates of the synchronous generators' rotor angle and active and reactive power; in real-time operation, only measurements of active and reactive power at the terminal of the synchronous generators are assumed. We first investigate the probability distribution of the dynamic states by means of Q-Q plots and verify that the states of the GM-KKF approximately follow a Student's t-distribution with 20 degrees of freedom when the initial state vector is normally distributed. Under this assumption, our filter presents high statistical efficiency. Then, we carry out numerical simulations on the IEEE 39-bus test system. They reveal that the GM-KKF has a faster convergence rate than the non-robust Koopman operator-based Kalman filter. They also show that the computing time of the GM-KKF is roughly reduced by one-third as compared to the one taken by our previously developed robust GM-extended Kalman filter.

Original languageEnglish (US)
Title of host publication2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538677032
DOIs
StatePublished - Dec 21 2018
Externally publishedYes
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: Aug 5 2018Aug 10 2018

Publication series

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

Conference

Conference2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Country/TerritoryUnited States
CityPortland
Period8/5/188/10/18

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

  • Dynamic state estimation
  • Kalman filter
  • Koopman operator
  • Nonlinear observer
  • Robust estimation
  • Robust statistics.

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