Reactive Power Optimization Based On Improved Particle Swarm Optimization Of Power System

With the increasing of power capacity and the electric equipment, the customer has asked more about power quality. Uneven distribution of reactive power will decrease voltage quality, transmission capability and increase network active loss. The reactive power optimization is effective means to reduce network losses and increase rate of qualified voltage, and finally enhance power supply quality. Studying the problem of reactive power optimization has the great significance in theory and practical application. After this paper elaborates the purpose and significance of power system reactive power optimization, development treads of optimal reactive control problem are introduced. Meanwhile, this paper compares advantages and disadvantages of kinds of optimization algorithms and its usable range, and research interests of reactive power optimization are proposed.Reactive power optimization of power system is mixed-integer and non-linear programming problem with multi-objective and multiple constraints. Its control variables consist of continuous variables and discrete variables, so the optimization becomes very complex. PSO is one of the intelligent optimization algorithms, For complex functions with high dimensions, PSO converges slowly and gets premature easily. For solving these disadvantages, Dynamical inertia weight vector is put forward to refine search for every dimension and improve convergence speed, and premature convergence is solved by bringing in dimension mutation operator. Besides, mutation-keeping strategy is proposed to raise mutation efficiency by providing more even mutation. This paper chooses minimization of active power loss as objective function, at the same time the bus voltages beyond limits and reactive power output beyond limits of generator are treated as penalty function.Numerical simulation software Matlab 7.0 is used to write the main program of PSO algorithm and calculating system flow with power system flow package Matpower. The experiments on six benchmarks show that improved PSO algorithm has faster convergence speed and better global search ability, and the results of tests on IEEE-14 and IEEE-30 nodal system prove that improved PSO algorithm is useful in solving power system reactive optimization.