Identification of the Source of Power Quality Degradation Using Signature Extraction from Voltage Waveforms

  • Parth VagheraEmail author
  • Dinesh Kumar
  • Nishant Kothari
  • Sayed Niamatullah
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 608)


This paper provides the understanding of features and method used for classifying the root cause of the fault event using those unique features. In particular, the paper focuses on identification of fault caused by the animal, lighting, tree, equipment and vehicle events. Different features are extracted to provide the input to neural network. For extracting features voltage and current samples collected during the fault at the monitoring, stations were analyzed. Artificial neural network with multilayer perceptron model is trained for the classification. Features were calculated using 154 real-world fault events and applied to the classifier. It estimated to be having 76.47% of classification rate.


Diagnosis (faults) Power quality disturbance Wavelet decomposition Artificial neural network Classification 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Parth Vaghera
    • 1
    Email author
  • Dinesh Kumar
    • 1
  • Nishant Kothari
    • 1
  • Sayed Niamatullah
    • 1
  1. 1.Department of Electrical EngineeringMarwadi Education Foundation’s Group of InstitutionRajkotIndia

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