Fault Location using Artificial Neural Network in a Radial Electric Power Distribution System with Recloser and Sectionalizers

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@article{1709149,
      author = {Eze Cosmas Chibuzo, Ashigwuike Evans, Ejimofor Chijioke },
      title = {Fault Location using Artificial Neural Network in a Radial Electric Power Distribution System with Recloser and Sectionalizers},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {837-862},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709149.pdf},
      abstract = {Promptly locating and clearing faults is key to maintaining the reliability of the power grid. The traditional way of locating faults on the grid cannot be relied upon due to the high costs involved with dispatching repair crew to search for the location of the fault and the time involved in achieving that. While the installation of protective devices such as reclosers and Sectionalisers help in protecting the network and preventing blanket outage of the network when a permanent fault occurs, they by themselves cannot locate the point on the network where the fault has occurred. However, the transients launched by the inception of a fault on the line contain features that could be used to trace the location of the fault. The main aim of this study is to employ artificial neural networks in locating faults in a distribution system involving reclosers and Sectionalisers. The data for training the artificial neural network is to be obtained by simulating various fault scenarios in MATLAB/Simulink and the features of the data are extracted by processing the data using the discrete wavelet transform (DWT). The modelling and simulation of the fault location system is done in MATLAB/Simulink on a 15 bus IEEE distribution network with a grid supply and a distributed generator connected to the system. Twenty (20) random trials were conducted on different lines of the network and the fault location system was able to identify the faulty line 60% of the times.},
      keywords = {Artificial Neural Network (ANN), Discrete wavelet Transform (DWT), Fault, Recloser, Sectionalizer.},
      month = {June},
  }