Application of an Improved Genetic Algorithm in Reactive Power Optimization

The genetic algorithm serves as an efficient global optimization search method for the reactive power management within power systems. To minimize active power losses and ensure that reactive power remains within designated limits, chaotic search techniques have been integrated with the traditional genetic algorithm, resulting in the development of an enhanced genetic algorithm that incorporates chaos search. This method initiates the population generation through chaotic sequence exploration and refines the crossover, mutation, and selection operators. These enhancements significantly bolster the global optimization capabilities of the genetic algorithm, effectively reduce the likelihood of local convergence, and improve both convergence speed and computational accuracy. A simulation study was conducted to optimize the system’s reactive power. The simulation results indicate that the improved genetic algorithm can more accurately identify the global optimum, significantly accelerates the convergence process, and outperforms the standard genetic algorithm.