Reactive Power Optimization Based On Nicle Particle Swarm Optimization Algorithm

Reactive power compensation and reactive power balance in the power system is one of the basic conditions to ensure the voltage quality. It plays an important role in guaranteeing the power system’s safe, stable and economy operation. Therefore, arranging a reasonable reactive power optimization on the grid and selecting the appropriate optimization objective function are great of significance for grid security and stability.In this paper, the impacts of voltage loss, active power, loss power factor which is caused by the reactive power will be described to illustrate the importance of reactive power compensation. And then analyzes the mathematical model of the common components of the power system. Finally, a mathematical model in the power system will be established. Its objective function is designed to be the cost of the invest of reactive power compensation equipment and active power loss of the system, in which the node voltages beyond limited and the generator reactive power output beyond limited are considered in the way of penalty function. The new objective function added investment and maintenance costs of reactive power compensation equipment.This could embody practical value of the algorithms more intuitively, in contrast to take active power loss as the objective function simply.In order to optimize the model, a new method called Niche Particle Swarm Optimization (NPSO) will be proposed. The algorithm does not require a priori value presented of niche radius, but obtain niche radius by calculating the Euclidean distance between the particles, which can improve the accuracy of the algorithm. The principle of division of niche groups is to find a ordinary particle as local extreme points (pbest), then the niche radius is the average of Euclidean distances with the pbest. If the Euclidean distance between the particles is less than or equal to the niche radius, the particle will join this niche particle swarm, otherwise they are excluded from the niche particle swarm. The niche technology can reduce the direct exchange of information between the particles and avoid the premature phenomenon of PSO, which could maintain the diversity of population and improve the adaptability of the evolved groups for enhancing the global searching ability.Finally, this paper take the IEEE-30as standard test cases to validate the optimization algorithm proposed and there were the following comparative contrast of four areas with PSO. By the contrast of node voltage contrast, all of the node voltages becomes qualified from original two node voltages beyond limited, which significantly improved the quality of voltage; By the contrast of the cost of the invest of reactive power compensation equipment and active power loss of the system, saving rate improved16.52%from the original14.38%, reflecting the economics of the algorithm; By the contrast of active power loss of the system, the drop rate reaches19.50%through niche particle swarm optimization (NPSO), which has an obvious effect on loss reduction; By the contrast of convergence speed and stability, algorithm have premature phenomenon of PSO, but NPSO significantly improves the probability distribution of the objective function on multi-peak and enhance the stability of the convergence because of niche technology. By comparing the simulation results of four groups, it reflects the validity and applicability of NPSO comprehensively and visually.