International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2569

Full Length Research Paper

Optimal transformer allocation in electrical distribution using genetic algorithm

Oluwole Charles Akinyokun
  • Oluwole Charles Akinyokun
  • Department of Computer Science, Federal University of Technology, Akure, Ondo State, Nigeria.
  • Google Scholar
Gabriel Babatunde Iwasokun
  • Gabriel Babatunde Iwasokun
  • Department of Computer Science, Federal University of Technology, Akure, Ondo State, Nigeria.
  • Google Scholar
Akinwale Michael Ojo
  • Akinwale Michael Ojo
  • Department of Computer Science, Federal University of Technology, Akure, Ondo State, Nigeria.
  • Google Scholar


  •  Received: 04 April 2014
  •  Accepted: 30 June 2014
  •  Published: 30 July 2014

Abstract

The optimization of transformers allocation is a major challenge to the operators of electrical energy distribution in several developing countries. In this research, a Generic Algorithm model for the optimization of transformer allocation in electrical distribution networks is developed. The algorithm employed the principles of selection, crossover and mutation to allocate transformers of different capacities to various substations in order to achieve their optimum performance. The objective function was subjected to cost and power capacity of each transformer as well as the growth rate and power consumption of the region. The initial population of chromosomes was generated at random with each consisting of potential solution to the problem. The chromosomes were decrypted and used to estimate the objective function. The GA operations were carried out on the chromosomes to know the ones that are best fit for consideration in the next generation. Results of a case study of transformer allocation in Osogbo District of Power Holding Company of Nigeria exhibited best-fit strategies for massive exchange (redistribution) of transformers in the district.
 
Key words: Genetic algorithm, transformer allocation, power distribution network, optimization and power generator.