Hybrid Cataclysmic Genetic Algorithm Used to Reactive Power Optimization

The reactive power optimization of power system is a complicated nonlinear mixed planning problem with multi-objectives. The optimizing computation using conventional linear and nonlinear methods has a disadvantage, the dispersed variable approximation, which makes it can´t correspond to the reality of reactive power optimization. Cataclysmic genetic algorithm (CGA) is a method of global optimization, which is suitable for mixed nonlinear planning problems with dispersed variables. Fuzzy-control method has an inherent merit for dealing with planning problems with soft constraints. To solve reactive power optimization problems with multi-objective functions, combining cataclysmic genetic algorithm with fuzzy control theory(HCGA) is a new effective method. Through the computation of a power system examples, comparing with the traditional genetic algorithm, this new algorithm is proved to have stable convergence, accurate and logical optimization results, moreover, a faster computation process. This new algorithm is competent of solving multi-objective mixed nonlinear planning problems.