Application of Catastrophic Adaptive Genetic Algorithm to Reactive Power Optimization of Power System

Abstract

In order to avoid the premature convergence and improve convergence rate, a catastrophic adaptive genetic algorithm for reactive power optimization is discussed in detail. Before the germination of premature convergence, the cataclysm operator is adopted to update all individuals randomly except for the current optimum.When the change rate of average fitness is decreased to a critical condition; the cataclysm operator will be implemented. In reproduction operator, the method of retaining optimal individual is used to ensure the convergence and at the same time, the competition method is also adopted to keep the better dispersal of all individuals. In Mutation operator, the mutation probability P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> is improved based on adaptive genetic algorithm. When fitness of individuals in the population tends to be identical, P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> can be adjusted to make bigger. The algorithm has been applied to IEEE 30-bus testing system .The test shows that the cataclysm operator can improve the diversity of the populations and avoid the premature convergence in genetic algorithm.