Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs

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

doi:10.1109/ISIE45063.2020.9152259

Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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 language	English
Title of host publication	2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	839-844
Number of pages	6
ISBN (Electronic)	9781728156354
DOIs	https://doi.org/10.1109/ISIE45063.2020.9152259
Publication status	Published - 2020 Jun
Event	29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands Duration: 2020 Jun 17 → 2020 Jun 19

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2020-June

Conference

Conference	29th IEEE International Symposium on Industrial Electronics, ISIE 2020
Country/Territory	Netherlands
City	Delft
Period	20/6/17 → 20/6/19

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Control and Systems Engineering

Access to Document

10.1109/ISIE45063.2020.9152259

Cite this

Lee, Y., Kim, S. H., Lee, G., & Shin, Y. J. (2020). Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings (pp. 839-844). Article 9152259 (IEEE International Symposium on Industrial Electronics; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE45063.2020.9152259

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title = "Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs",

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.",

keywords = "Dynamic state estimation, Kalman filter, particle filter, phasor measurement unit, unknown inputs.",

author = "Yonggu Lee and Kim, {Seon Hyeog} and Gyul Lee and Shin, {Yong June}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 ; Conference date: 17-06-2020 Through 19-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ISIE45063.2020.9152259",

language = "English",

series = "IEEE International Symposium on Industrial Electronics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings",

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Lee, Y, Kim, SH, Lee, G & Shin, YJ 2020, Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs. in 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings., 9152259, IEEE International Symposium on Industrial Electronics, vol. 2020-June, Institute of Electrical and Electronics Engineers Inc., pp. 839-844, 29th IEEE International Symposium on Industrial Electronics, ISIE 2020, Delft, Netherlands, 20/6/17. https://doi.org/10.1109/ISIE45063.2020.9152259

Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs. / Lee, Yonggu; Kim, Seon Hyeog; Lee, Gyul et al.
2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 839-844 9152259 (IEEE International Symposium on Industrial Electronics; Vol. 2020-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs

AU - Lee, Yonggu

AU - Kim, Seon Hyeog

AU - Lee, Gyul

AU - Shin, Yong June

PY - 2020/6

Y1 - 2020/6

N2 - 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.

AB - 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.

KW - Dynamic state estimation

KW - Kalman filter

KW - particle filter

KW - phasor measurement unit

KW - unknown inputs.

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Lee Y, Kim SH, Lee G, Shin YJ. Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 839-844. 9152259. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE45063.2020.9152259

Dynamic State Estimation of Generator Using PMU Data with Unknown Inputs

Abstract

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