Practical genetic algorithms
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A newly developed computationally enhanced steady state genetic algorithm (CSSGA) for optimizing the reactive power planning (RPP) in loss reduction and betterment of voltage profile in the power system is presented in this paper. CSSGA is a combined technique between the exploration factors and variant spin mechanism on steady state genetic algorithms (SSGA). The conventional genetic algorithm (GA) has the drawback of a sluggish convergent rate. Although the steady state genetic algorithm (SSGA) is time efficient but tend to produce finding lesser than the desired global solution. In this study, an optimum convergent centred SSGA method is implemented for the optimisation of reactive power planning via the combination of reactive power dispatch and transformer tap changer setting. The selection and steady state elitism combined with the conventional anchor spin techniques are incorporated into the development of the SSGA. The CSSGA is conducted by randomised resettlement of the chromosomes closer to the potential optimum solution. In each probing, identical initial population is supplied to the mechanism of SSGA and CSSGA in order to have consistency in the initial population. Traditionally, only a single selection is executed for selecting a string of variables. A variant spin technique is applied on the CSSGA, whereby the spin is conducted for every population of variables to induce further search space exploration. The proposed CSSGA techniques have been tested on the IEEE Reliability Test System (IEEE-RTS) and revealed competent performances in respect to the SSGA and elitist GA.