Discrete Wavelet Transform and kNN-Based Fault Detector and Classifier for PV Integrated Microgrid

  • Murli Manohar
  • Ebha Koley
  • Yuvraj Kumar
  • Subhojit Ghosh
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 38)

Abstract

The growing penetration of distributed energy resources (DERs) in modern power distribution networks operating as microgrid poses a great challenge for the conventional protection scheme due to significant variation in the fault current levels under the grid-connected and islanded mode of operation. In this regard, this paper has devised an efficient protection scheme based on discrete wavelet transform (DWT) and k-nearest neighbour (kNN) for fault detection/classification implemented for dual modes of microgrid operation considering the photovoltaic PV source and nonlinearity in the load. The proposed approach utilizes the three-phase voltage and current signals obtained during shunt faults in the distribution line under widely varying fault parameters. The pre-processing of signals through DWT determines the approximate coefficient. The standard deviation (SD) of the approximate coefficient so obtained is further fed as the input to the kNN-based classifier for fault detection/classification task separately for grid-connected and islanded mode. The test result analysis clearly reveals the effectiveness of the proposed approach and hence validates the performance.

Keywords

Microgrid Distributed energy resources (DERs) Discrete wavelet transform (DWT) k-nearest neighbour (kNN) Photovoltaic (PV) Fault detection and classification 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Murli Manohar
    • 1
  • Ebha Koley
    • 1
  • Yuvraj Kumar
    • 1
  • Subhojit Ghosh
    • 1
  1. 1.National Institute of TechnologyRaipurIndia

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