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State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools.

Lital Dabush1, Ariel Kroizer1, Tirza Routtenberg1,2

  • 1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces graph signal processing (GSP) methods for accurate power system state estimation, even with limited measurements. These novel techniques significantly improve accuracy and reduce sensor needs in unobservable systems.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Applied Mathematics

Background:

  • Power system state estimation is crucial for grid operation and control.
  • Conventional methods struggle with unobservable systems due to insufficient measurements.
  • Existing solutions often rely on inaccurate pseudo-data or unavailable large datasets.

Purpose of the Study:

  • To address the challenge of state estimation in power systems with limited measurements.
  • To develop novel methods overcoming information deficits in unobservable grids.
  • To propose an optimized sensor placement strategy for improved estimation.

Main Methods:

  • Validated the graph smoothness property of power system states (voltages) theoretically and empirically.
  • Developed a regularized graph signal processing weighted least squares (GSP-WLS) state estimator leveraging state smoothness.
Keywords:
graph signal processing (GSP)network observabilitypower system state estimation (PSSE)sensor allocation

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  • Introduced a sensor placement strategy tailored for the GSP-WLS estimator's performance optimization.

    • Main Results:

    • GSP methods reduced estimation error magnitude by up to two orders compared to existing approaches.
    • Achieved these results using only 70 sampled buses in the IEEE 118-bus system.
    • Improved bad data detection probability by up to 30% for the same false alarm rate in unobservable systems.

    Conclusions:

    • Proposed GSP methods enable accurate state estimation in unobservable power systems.
    • Significantly reduce the number of required measurement sensors.
    • Offer a robust solution for power system monitoring and control under data scarcity.