A Data-Driven Approach for High-Impedance Fault Localization in
  Distribution Systems
Yuqi Zhou Yuqing Dong Rui Yang
Accurate and quick identification of high-impedance faults 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.