Reactive Power Optimization Considered Load Change And Based On Improved 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 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. The problems existing in the application of reactive power optimization to practical power system and static reactive power optimization are analyzed, a novel algorithm, reactive power optimization considered load change and based on improved genetic algorithm is proposed in this paper. After a short-term load forecasting method based analogous and linear extrapolation is proposed, the load forecast and the priority of equipment action are led into static reactive power optimization. The aim function is constructed for the practical situation of power system. On the basis of traditional genetic algorithm the fitness function and the holding of population diversity are improved. The adjacent search operator is applied to improve ability of local search. The algorithm has good global convergence and convergence speed.Reactive power optimization considered load change and based on improvedgenetic algorithm is applied to Fujian province auto voltage control system, and the corresponding software is programmed. The practical application results show that with the improved genetic algorithm the action of control equipments is rational, the times of disperse equipment action are less, the voltage qualification rate can be effectively improved and the network loss is reduced and this application issuccessful.