Research Of Distribution Reactive Power Optimization Based On Immune Genetic Algorithm

Whether the reactive power distributes correctly has direct effect to the safety and stabilization of power system, also it has direct relation to the economy benefit. 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 quality and reduce the net real loss. So the study of the reactive power optimization has the great significance in theory and practical application.This paper consults a plenty of literatures and researches the methods of reactive power optimization. The algorithm mostly includes the tradition math optimization means, such as linear layout, nonlinear layout, mixed integral layout means, dynamic layout means, and artificial intelligence algorithm. The tradition math optimization means depend on accurate math model, but accurate math model is relatively complex and the solution is difficult, even it is hard to achieve the request of real time control. The coarse math models have bigger errors. In the last few years, the artificial intelligence optimization algorithms show particular advantages in the solution of more variable, nonlinear, discontinuous and more restriction problems. It makes up the shortages of the tradition math means and are regarded by people gradually in the area of reactive power optimization control.Today, Genetic algorithm is widely used in the area of reactive power optimization control on behalf of artificial intelligence algorithms. Genetic algorithm is one of the optimal search algorithms that are most widely in use. Today, genetic algorithm has been used in many fields, such of function optimum,model structure,structure optimum, and so on. Genetic algorithm is prone to fall into the local optimum. Sometimes, its convergence speed can not meet the demands also. How to overcome these limitations has been a primary question that Genetic algorithm faces.According to the characteristics of reactive power optimization and the limitation of genetic algorithm, this paper applies a new immune geneic algorithm to the reactive power research. base on the characteristic of natural immune system, this paper brings some operations, such as clone selection, clone proliferation, hypermutation, the death of uninspired cell, emergence of memory cell, and immune recruitment into genetic algorithm to improve genetic algorithm. The algorithm of IGA and SGA are similar on frame. But by adopting hypermutation and keeping the group of memory cell, the diversity of the colony is increased, at the same time, much and different individual are keeping, also these individuals are replaced in evolution, so the speed of gaining global optimum is increased. IGA adopts clone proliferation and immune recruitment operations. The former makes the algorithm search towards many directions in optimization spot of current pieces by mutating, so that the probability of gaining global optimum solution is increased; the latter guarantees diversity of the pieces and avoids the close competing, By adding new individuals in every generation, so the speed of gaining global optimum is increased.First, IGA are used in function optimization testing and parameter optimization tunning of PID controller. The simulation results show that IGA has rapider convergence and higher convergence precision than SGAThen, IGA is applied to the reactive power research. The problem of reactive power is a large-scale nonlinear optimization problem with a large number of variables and uncertain parameters, and its operation contains not only discrete variables but also continuous variables. this paper introduces the mixed coding of decimal integral and real number for the question of reactive power optimization, namely, the discrete variable are used integral coding by mapping. The truncation errors are avoided for the truncation decimal fraction in integral coding, while the unnecessary gene combinations are reduced in iterative optimization, so that the convergence speed is increased.The simulation experiment is that IGA is applied to IEEE-30 system, and the simulation results are compared to GA. The computing results show that the method of reactive power optimization based on IGA applied in this paper is effective, Compared with the traditional genetic algorithm, this algorithm improves the voltage quality, enhances the convergence speed and reduces the net loss of active power. So it is effective and possesses the excellent the value in theory and practice.