Application Of Improved Genetic Algorithm In Reactive Power Optimization Of Power System

With the development of national economy, the demands of power supply quality from all kinds of industries are increased. 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.In this paper, the study contents and present status of reactive power optimization are represented. After researched kinds of methods for reactive power optimization, the advantages and disadvantages and application of these methods are analyzed. A mathematical model for reactive power optimization is established to obtain minimization of network loss with satisfying constraints of power flow and security.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. As a kind of search algorithm is fairly fit for solution to the problem of reactive power optimization. Genetic algorithm is global convergent, but crossover operator and mutation operator are fixed relatively, so lacks alterable agility degree, which make genetic algorithm generating earliness phenomenon and getting into local extremum more easily. Therefore this paper adjusts crossover probability and mutation probability dynamically in course of evolution base on practice instance of population. Adaptive genetic algorithm keeps diversity of population, ensures astringency and advances optimization ability of algorithm. In addition, immune operator is applied to adaptive genetic algorithm, in precondition of reserve choiceness capability of original algorithm, restraining degeneration phenomenon in process of optimization much more availably.The proposed algorithm in this paper is applied to Ward&Hale6-bus and IEEE 30-bus system and the practical power system, and compares the result of standard genetic algorithm and improved genetic algorithm, the results verify that the proposed algorithm in this paper is effective and possesses the excellent the value in theory and practice, compared with the simple immune algorithm and adaptive genetic algorithm, this improved algorithm possesses the good global convergence and the rapid computing speed.