Hybrid optimization algorithm and its application for reactive power optimization of power systems

Hybrid optimization methods are used to resolve reactive power optimization with discrete and continuous variables, by which the complementary characteristics of genetic and traditional optimization algorithms are utilized.Genetic operations such as selection and crossover and mutation are acted on discrete variables only.The genetic algorithms are used to make the global searches to the population.Before a new offspring to select into the population it must to move to the local optimal point by using the traditional optimization methods to the continuous variables.To ensure the effects of the local optimization, a new optimization algorithm, which is based on function transform and generalized inverse of matrices, is used.The model of hybrid optimization algorithm is simplified and normal, which has the advantage of genetic algorithms treating discrete variables conveniently and the traditional optimization methods calculating fast and steady.The practicability and effectiveness of the algorithms are proved by case studies.