Research On Reactive Power Optimization In Power System Based On Immune Genetic Algorithm

Reactive power optimization in power systems is one of the most effective control methods to ensure power system operation securely and economically, and an important measure to improve the voltage profile and reduce the net real loss, so that the study of the problem of reactive power optimization has the great significance in theory and practical application.Reactive power optimization in power system is a mixed nonlinear optimization problem with many variables and constraints, the operating variables include continuous and discrete variables, so the optimization becomes very complicated. The measure of Reactive power optimization mainly considers on-load tap changer, optimal capacity of the capacitor, the voltage of generator under the steady load. Reducing active power loss and improve voltage quality is considered of the main object function in this paper. The model of reactive optimization was established based on these. And the penalty function is considered to deal with variables violating the constraints.Compared with Simulant Anneal, Tabu searehing and GA, we found that all these intellective algorithms have weakness, just like tend running into partial searehing. Immune genetic algorithm can keep individual diversity, avoid being trapped in the local convergence in the process of evolution and enhance the abilityof searching best solution. The function optimization test experiment by using Rosenbrock function verifies that the immune genetic algorithm is correct and convergent.Immune genetic algorithm is applied for reactive power optimization in electric power system. Based on the proposed mathematical model and algorithm, practical program is made by Matlab language. The proposed algorithm in this paper is applied to the IEEE 30-bus system and compares the results of optimization with the advanced genetic algorithm of other paper. The favourable effect can be obtained by using IGA in reactive optimization that is in node voltage control, power loss reduction anc seeking comprehensive benefit maximum value. All of the results show that the proposed algorithm in this paper has better ability of overall searching optimal solution and higher precision.