Research Of Reactive Power Optimization Based On Immune Genetic Algorithm

Reactive power optimization is one of the most important control methods to ensure power system operation securely and economically, and an effective measure to improve the voltage profile and reduce the transmission loss. Study the problem of reactive power optimization has the great significance in theory and practical application.Reactive power optimization is a large-scale nonlinear optimization problem with a large number of variables and uncertain parameters, the operating variables include continuous and discrete variables, so the optimization becomes very complex. According to the characteristics of reactive power optimization, a novel algorithm, immune genetic algorithm (IGA) is proposed in this paper. Based on improving the crossover and mutation operators, this algorithm inherits and develops the merits of genetic algorithm (GA) such as multi points searching, dealing with the discrete variables, application in large-scale range and so on. According to the shortage of GA converging to a local optimal solution because of reducing the diversity of individuals, the theory of biological immune system is cited, the immune operators including calculation the densities of antibodies, activating or suppressing antibodies and making the memory cell are designed, and effectively combined with GA operators. With self-regulation of antibodies, IGA greatly improves the diversity of antibodies, achieves a good dynamic balance between individual diversity and population global convergence, and avoids getting into the local optimal solution.IGA is applied to the problem of reactive power optimization of power system, and the corresponding software is programmed. The software adopts the method of object oriented programming, so theprogram is convenient to modify, debug, transplant, and compute the different systems.The computing results against the IEEE 30-bus system and the actual network prove that the method of reactive power optimization based on IGA proposed in this paper is effective and possesses the excellent the value in theory and practice. Compared with the traditional genetic algorithm, IGA possesses the good global convergence and the rapid computing speed.XU Chunli ( Electric Power System and Automation) Directed by Prof. HUANG Wei...