Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs

Yonggu Lee, Seon Hyeog Kim, Gyul Lee, Yong June Shin

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

Abstract

In this paper, a Kalman filter and particle filter based dynamic state estimation method are proposed for nonlinear systems with unknown inputs. In the proposed method, the dynamic states of a generation system are estimated in three stages. At the first stage, the biased states are predicted using unscented transform without unknown inputs. At the second stage, the unknown inputs are estimated using a particle filter technique with phasor measurements and the predicted biased states. At the final stage, the unbiased states are estimated using an unscented Kalman filter method with the estimated unknown inputs. The proposed algorithm is implemented in Korean power system model, and is compared with dynamic state estimation performances of other estimation algorithms with unknown inputs.

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages839-844
Number of pages6
ISBN (Electronic)9781728156354
DOIs
Publication statusPublished - 2020 Jun
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
Duration: 2020 Jun 172020 Jun 19

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2020-June

Conference

Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020
CountryNetherlands
CityDelft
Period20/6/1720/6/19

Bibliographical note

Funding Information:
This research was supported by Korea Electrotechnology Research Institute (KERI) Primary research program through the National Research Council of Science & Technology (NST) funded by the Ministry of Science and ICT (MSIT) (No. 19-12-N0101-04). This research was also supported by Korea Electric Power Corporation (KEPCO) #R18XA05.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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