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A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

Author

Listed:
  • Fujia Han

    (Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK)

  • Phillip M. Ashton

    (Network Operations, National Grid, Wokingham RG41 5BN, UK)

  • Maozhen Li

    (Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK)

  • Ioana Pisica

    (Brunel Institute of Power Systems, Brunel University London, London UB8 3PH, UK)

  • Gareth Taylor

    (Brunel Institute of Power Systems, Brunel University London, London UB8 3PH, UK)

  • Barry Rawn

    (Brunel Institute of Power Systems, Brunel University London, London UB8 3PH, UK)

  • Yi Ding

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310000, China)

Abstract

Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations.

Suggested Citation

  • Fujia Han & Phillip M. Ashton & Maozhen Li & Ioana Pisica & Gareth Taylor & Barry Rawn & Yi Ding, 2021. "A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering," Energies, MDPI, vol. 14(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2166-:d:535238
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    References listed on IDEAS

    as
    1. Farhan Mahmood & Hossein Hooshyar & Luigi Vanfretti, 2016. "Extracting Steady State Components from Synchrophasor Data Using Kalman Filters," Energies, MDPI, vol. 9(5), pages 1-15, April.
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