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Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

Author

Listed:
  • Jiang Li

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Wenzhen Wei

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Shuo Zhang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Guoqing Li

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Chenghong Gu

    (Department of Electronic & Electrical Engineering, University of Bath, Bath BA2 7AY, UK)

Abstract

Micro phasor measurement units (μPMU) installed in active distribution networks are very useful for improving observability by acquiring system real-time data. However, three-phase imbalance and harmonic power flows adversely impact the accuracy of synchronous measurements, which implies the importance of phasor estimation errors. This paper proposes a new phasor estimation algorithm for μPMU in active distribution networks that uses a conditional maximum likelihood (CML) estimation method. Firstly, the signal model of three-phase, three-wire and four-wire imbalance systems is established. Then, the probability distributions of the magnitude and phase angles are derived from the geometric characteristics of the CML method by solving the geometric equation. Simulation results show that the proposed CML based method is effective for estimating phasor and impedance models of active distribution networks by using μPMU measurement data.

Suggested Citation

  • Jiang Li & Wenzhen Wei & Shuo Zhang & Guoqing Li & Chenghong Gu, 2018. "Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks," Energies, MDPI, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1320-:d:148382
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    References listed on IDEAS

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    1. Heng-Yi Su & Tzu-Yi Liu, 2017. "A PMU-Based Method for Smart Transmission Grid Voltage Security Visualization and Monitoring," Energies, MDPI, vol. 10(8), pages 1-16, July.
    2. Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
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    Cited by:

    1. Malgorzata Binek & Andrzej Kanicki & Pawel Rozga, 2021. "Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System," Energies, MDPI, vol. 14(9), pages 1-14, April.

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