• Title of article

    Optimal Capacitor Placement in Radial Distribution Network Based on Power Loss Sensitivity Index Using Ant Lion Optimizer Considering Different Loading

  • Author/Authors

    Bakhtiari, Mehdi Department of Electrical Engineering - Bushehr Branch - Islamic Azad University , Mallaki, Mehrdad Department of Electrical Engineering - Bushehr Branch - Islamic Azad University , Moaddabi, Nima Department of Electrical Engineering - Bushehr Branch - Islamic Azad University

  • Pages
    13
  • From page
    73
  • To page
    85
  • Abstract
    In this paper, the reactive resources placement including capacitor bank in radial distribution network is studied. The placement purpose is to reduce the cost of power loss, the cost of capacitor purchase and installation. The location and size of the capacitors in the distribution network are determined using the intelligent ant lion optimizer (ALO) method, which is inspired by the hunting behavior of the ant lions. Based on the power loss sensitivity factor (LSF), candidate buses are selected for capacitor installation using the ALO. The proposed method is implemented ona 33-bus radial distribution networks. In this study, the effect of loading changes on the placement problem and distribution network characteristics including power losses, minimum voltage, voltage profile and net savings are evaluated. The results show that after optimal capacitor placement the characteristics of the distribution network includes active and reactive power loss are significantly reduced and also the network voltage profile is improved compared to former capacitor placement. The performance of the proposed method is compared to particle swarm optimization (PSO), teaching-learning based optimization (TLBO) and previous studies, which showed the superiority of the proposed method in achieving lower cost and greater net saving.
  • Keywords
    Radial Distribution Network , Capacitor Placement , Power Loss Sensitivity Index , Cost , Ant Lion Optimizer
  • Journal title
    Signal Processing and Renewable Energy
  • Serial Year
    2020
  • Record number

    2533741