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Grid Topology Identification With Hidden Nodes via Structured Norm Minimization.

Rajasekhar Anguluri1, Gautam Dasarathy1, Oliver Kosut1

  • 1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA.

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This study identifies electric distribution grid topology using voltage data and inverse covariance matrix patterns. The novel algorithm effectively identifies grid structures, even with hidden buses, for improved power system analysis.

Keywords:
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Area of Science:

  • Electrical Engineering
  • Network Science
  • Optimization Theory

Background:

  • Accurate electric distribution grid topology is crucial for reliable power system operation and control.
  • Identifying grid topology, especially with unobservable (hidden) buses, presents a significant challenge in power systems engineering.
  • Existing methods may struggle with the complexity introduced by hidden buses and sparse network structures.

Purpose of the Study:

  • To develop a novel algorithm for identifying the topology of electric distribution grids.
  • To address the challenge of hidden buses in topology identification.
  • To leverage the sign patterns of the inverse covariance matrix for accurate grid structure determination.

Main Methods:

  • Utilizing the sign patterns of the inverse covariance matrix of bus voltage magnitudes and angles.
  • Expressing the inverse covariance matrix as a sum of structured matrices (sparse, low-rank with sparse factors, low-rank).
  • Formulating and solving a convex optimization problem with sparsity and structured norm constraints via alternating minimization.

Main Results:

  • Successfully identified the topology of a distribution grid with a minimum cycle length greater than three.
  • The proposed convex optimization approach effectively estimates structured matrices from empirical data.
  • Algorithm performance validated on a modified IEEE 33 bus system, demonstrating practical applicability.

Conclusions:

  • The developed algorithm provides an effective method for electric distribution grid topology identification, even with hidden buses.
  • The approach based on inverse covariance matrix sign patterns and convex optimization is robust and accurate.
  • This work contributes to enhanced power system monitoring and control through improved topology awareness.