Abstract :
In the present paper, bases on modifications to the coding method, selection strategy, and crossover and mutation operations, a novel genetic algorithm (GA) is proposed for the reactive power optimization (RPO). Through hybrid coding of integer and real number, the proposed algorithm can account for both of the continuous and discrete control variables. Also, a comprehensive evolutionary selection methodology is applied, with different reproduction patterns at different stages. The operations of arithmetic crossover and mutation are determined according to the types of their variables, and their probabilities vary with the evolution loop. The modified GA is deployed on the IEEE-14, IEEE-30, and IEEE-57 bus systems for effectiveness evaluation. Simulation results indicate that the proposed algorithm help to speed up the RPO convergence and to enhance the global optimization performance.
Keywords :
genetic algorithms; probability; reactive power; IEEE-14 bus systems; IEEE-30 bus systems; IEEE-57 bus systems; RPO convergence; coding method; continuous control variable; crossover operation; discrete control variable; evolution loop; genetic algorithm; global optimization; mutation operation; probabilities; reactive power optimization; selection strategy; Convergence; Encoding; Generators; Genetic algorithms; Optimization; Reactive power; Active Power Loss; Comprehensive Selection Strategy; Genetic Algorithm; Hybrid Coding; Reactive Power Optimization;
