An improved genetic algorithm for reactive power optimization

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.