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Time-Domain Voltage Sag State Estimation Based on the Unscented Kalman Filter for Power Systems with Nonlinear Components

Author

Listed:
  • Rafael Cisneros-Magaña

    (División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica S/N, Morelia, Michoacán 58030, Mexico)

  • Aurelio Medina

    (División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica S/N, Morelia, Michoacán 58030, Mexico)

  • Olimpo Anaya-Lara

    (Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1RX, UK)

Abstract

This paper proposes a time-domain methodology based on the unscented Kalman filter to estimate voltage sags and their characteristics, such as magnitude and duration in power systems represented by nonlinear models. Partial and noisy measurements from the electrical network with nonlinear loads, used as data, are assumed. The characteristics of voltage sags can be calculated in a discrete form with the unscented Kalman filter to estimate all the busbar voltages; being possible to determine the rms voltage magnitude and the voltage sag starting and ending time, respectively. Voltage sag state estimation results can be used to obtain the power quality indices for monitored and unmonitored busbars in the power grid and to design adequate mitigating techniques. The proposed methodology is successfully validated against the results obtained with the time-domain system simulation for the power system with nonlinear components, being the normalized root mean square error less than 3%.

Suggested Citation

  • Rafael Cisneros-Magaña & Aurelio Medina & Olimpo Anaya-Lara, 2018. "Time-Domain Voltage Sag State Estimation Based on the Unscented Kalman Filter for Power Systems with Nonlinear Components," Energies, MDPI, vol. 11(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1411-:d:149996
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    References listed on IDEAS

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    1. Rusi Chen & Tao Lin & Ruyu Bi & Xialing Xu, 2017. "Novel Strategy for Accurate Locating of Voltage Sag Sources in Smart Distribution Networks with Inverter-Interfaced Distributed Generators," Energies, MDPI, vol. 10(11), pages 1-20, November.
    2. Mohammad Shoaib Shahriar & Ibrahim Omar Habiballah & Huthaifa Hussein, 2018. "Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance," Energies, MDPI, vol. 11(3), pages 1-15, March.
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    Cited by:

    1. Alexandre Serrano-Fontova & Pablo Casals Torrens & Ricard Bosch, 2019. "Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies," Energies, MDPI, vol. 12(16), pages 1-21, August.
    2. Jiong Wang & Hua Zhang & Dongliang Lin & Huibin Feng & Tao Wang & Hongyan Zhang & Xiaoding Wang, 2020. "A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities," Future Internet, MDPI, vol. 12(2), pages 1-12, February.
    3. Yuanqian Ma & Xianyong Xiao & Ying Wang, 2018. "Investment Strategy and Multi–Objective Optimization Scheme for Premium Power under the Background of the Opening of Electric Retail Side," Energies, MDPI, vol. 11(8), pages 1-25, August.
    4. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.

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