Distribution Network Reactive Power Optimization And Tracking Adjustment Technology Research

Electricity is essential to energy production and life, Plays an irreplaceable role in the development of the whole society. Reactive power optimization of power system plays a key role in achieving safe and efficient operation of the distribution network in, and also attracted many exexperts and scholars to explore them in depth.Reactive power optimization of power system is a system to meer all operating under the constraints of the situation, by the amount of reactive power compensation equipment investment and generator terminal voltage control, On load tap tap to location determinaition, to make the system meet the objective functin minimem loss of active network. In the system parameters to optimize the calculatin, so as to enhance the voltage quality and reduce power loss in the network, to ensure that the purpose of economic operation of power system stability. Reactive power optimization regers to the traditional concept of a static reactive power potimization, the method only section of a typical load potimization study, however, in practve the study, due to grid load changes over time, for the actual poeration of the grid. Using single static optimization dose not fully meet the requirements, andthe dynamic reactive power optimization of the actual situation of the moment load changes were considered. On the basis functions satisfy the system constraints and the limited number of devices on the adjustment, optimal control equipment running program, reaching over a period of time significantly reduce active network losses and improve voltage quality.In this paper, the improved AFSA based dynamic reactive power optimization of power system studies, and take into account the changes in device paramenters of the system at different times of. Inorder to ensure economic operation of power systems, in order to meet the actual constraints of equipment, as well as the results of a reliable model, based on the day’s load changes cluster sub-period of the study, the establishment of a dynamic model of reactive power oprimization. The improved AFSA is applied n the hour with continuous adjustment to optimize the characeeristics of the control verivble, improve and optimize the efficiency of the system to optimize accuracy, forming optimization based on improved AFSA dynamic reactive power policy. In this paper, IEEE14 node standard test system, the results show that the improved AFSA with traditional genetic algorithm also has high convergence precision, speed, reliability, superior features, can be effectively applied to the distribution network reactive power optimization.