Research On Approaches Of Reactive Power Optimization

Reactive power optimization of power system is not only an effective means to ensure power system operation securely and economically, but also one of the most important methods to improve the voltage quality and reduce the transmission loss. In theory and practical application, the study to reactive power optimization of power system is of great significance.Reactive power optimization of power system is a large-scale nonlinear programming problem with a large number of variables and constraint conditions, and the control variables are made of continuous and discrete variables, so reactive power optimization problem is very complex. According to the characteristics of reactive power optimization, a mixed algorithm—tabu search genetic algorithm(TSGA) is proposed in this paper. Based on the improved encoded mode, crossover and mutation operators, the mixed algorithm inherits and develops the merits of genetic algorithm(GA), such as multiple-point searching, strong robustness. Considering the shortage of GA, such as the poor hill climbing capacity and local convergence, and the characteristics of tabu search (TS), such as the strong hill climbing capacity, the fast rate of converge and single-point searching, the paper combines the GA with TS. In TSGA, whenever the population has the trend to converge to a local optimal solution, TSGA strengthens the hill climbing capacity to avoid converging to a local optimal solution by the use of TS.Making use of MATLAB language, the paper compiles the TSGA program for reactive power optimization of power system. The computing results against the IEEE 30-node system prove that the method of reactive power optimization based on TSGA proposed in this paper is right and effective. Compared with the GA and the TS, TSGA possesses the better global convergence and the quicker rate of converge.