Optimal Allocation of Automatic Reclosers

  • Carlos Frederico Meschini Almeida
  • Gabriel Albieri Quiroga
  • Henrique Kagan
  • Nelson Kagan
Part of the Power Systems book series (POWSYS)


This chapter presents a methodology for the allocation of Automatic Reclosers (AR) in medium voltage electric distribution networks. The methodology defines strategic positions for installing Normally Closed (NC) and Normally Opened (NO) reclosers to improve the system’s performance in terms of quality of power supply. The restriction relies on the budget available for investing in purchasing and installing AR. The methodology supports power distribution planning activities, as it focusses on defining the optimal positions for installing reclosers in a large network. Due to the size of the electric distribution networks considered during planning activities, hundreds different positions for installing Normally-Opened Automatic Reclosers (NO-AR) and Normally-Closed Automatic Reclosers (NC-AR) must be assessed. To deal with the size of the problem, covering all states the network may assume and assuring the positions for installing AR were optimum ones, the proposed methodology divides this problem into three states. Through this approach, the planning engineer need to carry out several simulations in just a few minutes, evaluating the technical benefits achieved from different investment levels. Similar approaches could not be found in the current literature. The methodology was assessed considering two substations of a Brazilian electric distribution company, corresponding to twenty-five medium voltage feeders. Two analyses were carried out: the brown field analysis, where the positions of thirty new automatic reclosers were determined; and the green field analysis, where forty-five existing automatic reclosers were reallocated. The results indicate significant improvements in quality of service indices, which may reach over 30% reduction level.


Electric distribution planning Genetic algorithms Electric distribution reliability Automatic reclosers Power quality 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Carlos Frederico Meschini Almeida
    • 1
  • Gabriel Albieri Quiroga
    • 1
  • Henrique Kagan
    • 2
  • Nelson Kagan
    • 1
  1. 1.Escola PolitecnicaUniversidade de Sao PauloSao PauloBrazil
  2. 2.Sinapsis Inovacao em EnergiaSao PauloBrazil

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