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Extracting Steady State Components from Synchrophasor Data Using Kalman Filters

Author

Listed:
  • Farhan Mahmood

    (Department of Electric Power & Energy Systems, The Royal Institute of Technology, Stockholm 10044, Sweden)

  • Hossein Hooshyar

    (Department of Electric Power & Energy Systems, The Royal Institute of Technology, Stockholm 10044, Sweden)

  • Luigi Vanfretti

    (Department of Electric Power & Energy Systems, The Royal Institute of Technology, Stockholm 10044, Sweden
    Statnett Statsforetak, Oslo 0423, Norway)

Abstract

Data from phasor measurement units (PMUs) may be exploited to provide steady state information to the applications which require it. As PMU measurements may contain errors and missing data, the paper presents the application of a Kalman Filter technique for real-time data processing. PMU data captures the power system’s response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter methods have been applied to extract the steady state components of PMU measurements that can be fed to steady state applications. Two KF-based methods have been proposed, i.e. , a windowing-based KF method and “the modified KF”. Both methods are capable of reducing noise, compensating for missing data and filtering outliers from input PMU signals. A comparison of proposed methods has been carried out using the PMU data generated from a hardware-in-the-loop (HIL) experimental setup. In addition, a performance analysis of the proposed methods is performed using an evaluation metric.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:315-:d:68865
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    Citations

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    Cited by:

    1. Sandra Castano-Solis & Daniel Serrano-Jimenez & Lucia Gauchia & Javier Sanz, 2017. "The Influence of BMSs on the Characterization and Modeling of Series and Parallel Li-Ion Packs," Energies, MDPI, vol. 10(3), pages 1-13, February.
    2. 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.

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