Comparative Study of Various Steady State Genetic Algorithms Equipped with Two Point Cross Breeding Method for Optimal Reactive Power Planning in Loss Minimisation Scheme


This paper examines the comparative performances between a computationally enhanced steady state genetic algorithm (CSGA) and standard steady state genetic algorithms (SSGA) in improving the voltage profile and conducting reactive power planning (RPP) in respect to loss minimization scheme. The selection elitism associated with the variantpopulation spin technique and steady state mechanisms are incorporated into the development of the SSGA. The CSGA probabilistically reposition the chromosomes around the potential optimum solution while deploying extra preventive mechanism to inhibit from trapping onto a local minimum. Each method of the CSGA and SSGA is individually engaged for implementing an ideal RPP via the combination of reactive power dispatch and transformer tap changer setting. Identical initial population is provided to the individual mechanism of SSGAs and CSGAs for obtaining consistency in the control initial population during each investigation. The two-point cross breeding is implemented for better linkage with the previous generation. The recommended CSGA techniques have been tested on the IEEE Reliability Test System (IEEE-RTS) and demonstrated competent performances in comparison to the SSGA.

  • Abstract
  • Key Words
  • 1. Introduction
  • 2. Problem Formulation
  • 3. Steady State Genetic Algorithm (SSGA)
  • 4. Computationally Enhanced Steady State Genetic Algorithm (CSGA)
  • 5. Result and Discussions
  • 6. Conclusion
  • References

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