A Hybrid Genetic Algorithm Approach for Optimal Power Flow
This paper puts forward a reformed hybrid genetic algorithm (GA) based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA), adaptive genetic algorithm (AGA), differential evolution (DE), particle swarm optimization (PSO) and music based harmony search (MBHS) on a IEEE30 bus test bed, with a total load of 283.4 MW. It’s found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.
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