  This paper proposes an accurate High Impedance Fault (HIF) detection and
isolation scheme in a power distribution network. The proposed schemes utilize
the data available from voltage and current sensors. The technique employs
multiple algorithms consisting of Principal Component Analysis, Fisher
Discriminant Analysis, Binary and Multiclass Support Vector Machine for
detection and identification of the high impedance fault. These data driven
techniques have been tested on IEEE 13-node distribution network for detection
and identification of high impedance faults with broken and unbroken conductor.
Further, the robustness of machine learning techniques has also been analysed
by examining their performance with variation in loads for different faults.
Simulation results for different faults at various locations have shown that
proposed methods are fast and accurate in diagnosing high impedance faults.
Multiclass Support Vector Machine gives the best result to detect and locate
High Impedance Fault accurately. It ensures reliability, security and
dependability of the distribution network.
