A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems: Preprint

Yuqi Zhou, Yuqing Dong, Rui Yang

Research output: Contribution to conferencePaper


Accurate and quick identification of high-impedance faults (HIFs) is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage-current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage-current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification.
Original languageAmerican English
Number of pages8
StatePublished - 2024
Event2024 IEEE PES GENERAL MEETING - Seattle, Washington
Duration: 21 Jul 202425 Jul 2024


CitySeattle, Washington

NREL Publication Number

  • NREL/CP-5D00-87945


  • explainable artificial intelligence
  • high-impedance fault
  • machine learning
  • piecewise approximation
  • support vector machine
  • voltage-current trajectory


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