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Research Papers

Single-Machine Infinite-Bus Power System Excitation Control Design With Resilient Extended Kalman Filter

[+] Author and Article Information
Xin Wang

Assistant ProfessorDepartment of Electrical and Computer Engineering,
Southern Illinois University,
Edwardsville, IL 62026
e-mail: xwang@siue.edu

Patrick Gu

Department of Electrical and Computer Engineering,
Southern Illinois University,
Edwardsville, IL 62026
e-mail: pgu@siue.edu

1Corresponding author.

Manuscript received February 25, 2016; final manuscript received June 19, 2016; published online November 21, 2016. Assoc. Editor: Konstantin Zuev.

ASME J. Risk Uncertainty Part B 3(1), 011001 (Nov 21, 2016) (9 pages) Paper No: RISK-16-1069; doi: 10.1115/1.4034018 History: Received February 25, 2016; Accepted June 22, 2016

To effectively control and maintain the transient stability of power systems, traditionally, the extended Kalman filter (EKF) is used as the real-time state estimator (RTSE) to provide the unmeasurable state information. However, the EKF estimation may degrade or even become unstable when the measurement data are inaccurate through random sensor failures, which is a widespread problem in data-intensive power system control applications. To address this issue, this paper proposes an improved EKF that is resilient against sensor failures. This work focuses on the resilient EKF’s (REKF’s) derivation with its application to single-machine infinite-bus (SMIB) power system excitation control. The sensor failure rate is modeled as a binomial distribution with a known mean value. The performance of REKF is compared with the traditional EKF for power system observer-based control under various chances of sensor failures. Computer simulation studies have shown the efficacy and superior performance of the proposed approach in power system control applications.

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References

Figures

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Fig. 1

SMIB power system circuit diagram

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Fig. 2

Equivalent circuit of the SMIB power system

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Fig. 3

x1-state trajectory at 5% error rate

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Fig. 4

x1-state trajectory at 8% error rate

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Fig. 5

x1-state trajectory at 11% error rate

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Fig. 6

x1-state trajectory at 18% error rate

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Fig. 7

Average RMS estimation error comparison between REKF and EKF at 5% error rate

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Fig. 8

Average RMS estimation error comparison between REKF and EKF at 8% error rate

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Fig. 9

Average RMS estimation error comparison between REKF and EKF at 11% error rate

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Fig. 10

Average RMS estimation error comparison between REKF and EKF at 18% error rate

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Fig. 11

Measurement y1 corrupted by bad data at 5% error rate

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Fig. 12

Measurement y1 corrupted by bad data at 8% error rate

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Fig. 13

Measurement y1 corrupted by bad data at 11% error rate

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Fig. 14

Measurement y1 corrupted by bad data at 18% error rate

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