Power System Reactive Power Optimization Based On Particle Swarm Optimization Algorithm

Reactive power optimization is of great significance to ensure the safety,stability and economic operation of the power grid.It is a kind of multi-objective nonlinear problem with mixed constraints and can be divided into reactive operation optimization and reactive planning optimization.Although there’re two different optimization objectives,both methods are enabling one or more performance index to be the best through control variables in constraint conditions.The study mainly aims at minimizing network loss,Newton-Raphson power flow algorithm combined with improved particle swarm optimization algorithm is adopted to reduce the loss of each branch in the power network,then achieve the purpose of reactive power optimization.The paper mainly introduces the reactive power optimization’s background in the domestic and overseas.The advantages and disadvantages of classical and intelligence reative power optimization methods are summarized.In this study control variables are generator voltage,transformer ratio and capacitor reactive power,load voltage and generator reactive power are state variables.In view of equality constraints of node active and reactive power and variables’ inequality constraints,improved particle swarm optimization will be used to realize the optimization of power network.The main process of the study is: collecting power system data and starting power flow calculation,then getting the optimized data;after the process;optimizing the data again by improved particle swarm optimization algorithm and get final result;analyzing results before and after algorithm improvement with initial network loss.In order to solve the concentration problem of PSO in the later evolution period,the paper presents an algorithm adaptive particle swarm optimization algorithm based on information selection(AISPSO).Being different with traditional particle swarm optimization’s group sharing experience of particle update,AISPSO will combine with the different behavior of neighborhood particles.Then it compares the fitness of the particles and chooses better ones to replace the worse ones.Finally,in the optimizing process,the number of particles is constant and the diversity of particles increases,then it optimizes the local convergence.The improved PSO is applied to IEEE30 and IEEE57 in power system reactive power optimization and compare the active power loss with others,there’s a significant decrease.From the compared data and curve,it can be sure that AISPSO can reduce the power network loss and improve the stability and operating efficiency.