Research And Realization Of Reactive Power Optimization Algorithm For Power Grid

With the increasingly complex network structure and the rapid development of smart grid,intelligent scheduling system required to achieve efficient and workable reactive voltage regulation scheme.So,how to further enhance the ability of reactive power and voltage regulation,reactive power optimization to improve the efficiency of power system becomes a problem for researchers.Optimized control reactive power and voltage power system is one of the basic tasks scheduled to run,refers to the reactive power is more abundant,by adjusting the generator terminal voltage,transformer tap ratio,output and other reactive power compensation device measures adjusting the reactive power flow,making the system voltage reaches a qualified value,while reducing network losses.The existing reactive power optimization models are: single target model,multi-objective dynamic optimization model,etc;The optimization algorithms include the traditional algorithms such as linear programming method and simplified gradient method,and intelligent algorithms such as genetic algorithm and simulated annealing algorithm.In practical application,although there are strict theoretical support,the planning class algorithm is difficult to deal with the problem of large number of discrete variables and constraints,and the real-time control of the complex reactive power optimization model is not up to the requirement.Therefore,in order to solve the difficulty of reactive power optimization in practice,this paper studies the design of particle swarm optimization algorithm with variable inertia weight and acceleration factor after comprehensive analysis of the status of scientific research in the category of reactive power optimization.In the formula,the inertia weight w and the acceleration factor c2 change the values of w and c2 according to the distance between the particles and the optimal particle.When the particles are close to the population optimal particle,increase the inertia weight w and reduce the acceleration factor c2.At the same time,the algorithm is compared with genetic algorithm,simulated annealing algorithm and ant colony algorithm.The experimental results show that the algorithm is effective in reducing power loss,and the optimization time is short.Finally,the improved algorithm is applied to the reactive power optimization visualization software system of Sichuan Zigong Power Grid based on struts2 framework and oracle database.