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Application of Machine Learning Based Technique for High Impedance Fault Detection in Power Distribution Network

  • Katleho MoloiEmail author
  • Jaco JordaanEmail author
  • Yskandar HamamEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

High-impedance faults (HIFs) detection with high reliability has been a prominent challenge for protection engineers over the years. This is mainly because of the nature and characteristics this type of fault has. Although HIFs do not directly pose danger to the power system equipment, they pose a serious threat to the public and agricultural environment. In this paper, a technique which comprises of a signal decomposition technique, feature extraction, feature selection and fault classification is proposed. A practical experiment was conducted to validate the proposed method. The scheme is implemented in MATLAB and tested on the machine intelligence platform WEKA. The scheme was tested on different classifiers and showed impressive results for both simulations and practical cases.

Keywords

Fault classification Fault detection Feature extraction High impedance fault Power system Fault detection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringTshwane University of TechnologyPretoriaSouth Africa

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