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Open Access Publications from the University of California

Data-driven approach for distribution network topology detection

  • Author(s): von Meier, Alexandra;
  • Poolla, Kameshwar;
  • Arghandeh, Reza;
  • Cavraro, Guido
  • et al.

This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on the real time analysis of time-series voltages measurements. The analysis approach draws on data from high-precision phasor measurement units (μPMUs or synchrophasors) for distribution networks. The key fact is that time-series measurement data taken from the distribution network has specific patterns representing state transitions such as topology changes. The proposed algorithm is based on comparison of actual voltage measurements with a library of signatures derived from the possible topologies simulation. The IEEE 33-bus model is used for the algorithm validation.

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