Enhancement of 11kV Distribution Network for Power Quality Improvement Using Artificial Neural Network Based DVR

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@article{1703351,
      author = {Chikezie, Okechi, Prof. Dikio Clifford Idoniboyeobu, Dr. Sepiribo Lucky Braide, Uwho, Kingsley Okpara},
      title = {Enhancement of 11kV Distribution Network for Power Quality Improvement Using Artificial Neural Network Based DVR},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {10},
      pages = {102-111},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703351.pdf},
      abstract = {Voltage sag and swell are major challenges facing Rumuomoi 11kV distribution network and no mitigationor control by means of custom power devices has been considered. This Research work is aimed at addressing these power quality  challenges in Rumuomoi 11kV distribution network using artificial neural network (ANN) controller based dynamic voltage restorer. The artificial neutral network controller used to control the dynamic voltage restorer was trained with the input and target data obtained from simulation with PI controller. Matlab Simulink software is used for this research. The proposed dynamic voltage restorer system was tested with replicated model of Rumuomoi 11kV distribution network. The result obtained shows that Bus 7 with 0.938p.u, Bus 8 with 0.9244p.u, Bus 9 with 0.9148p.u, Bus 10 with 0.9035p.u, Bus 11 with 0.8912p.u and Bus 12 with 0.8811p.u violated the statutory limit condition of 0.95-1.01p.u. After optimization of the network using DVR, there was no bus voltage violation which shows that DVR was effective in improving voltage profile as well as mitigating voltage sag and swell from the distribution network.},
      keywords = {Enhancement, Distribution, Power Quality, Artificial Neural Network, DVR.},
      month = {April},
  }