Reaactive Power Optimization Of Power System Based Improved Particle Swarm Optimization

Reactive power optimization is a challenging project in the field of power system[1]. The optimization and adjustment of reactive power is significant for the safe and stable operation of power grid. Reactive power optimization of power system is a nonlinear programming problem with multiple variables and constraints of complex mixed problems because of various calculation methods currently used to optimize itself have certain limitations, cannot perfectly solve the optimization problem, so we still need to continue to explore new ways to solve these problems.Based on particle swarm optimization algorithm for reactive power optimization despite the further research of experts and scholars at home and abroad has made great progress[2], but due to the limitation of the particle swarm algorithm itself, coupled with the diversity of changes in the parameters of the algorithm, the particles swarm optimization algorithm is still a lot of room for improvement. Based on this idea, this paper focuses on the analysis of reactive power optimization in power system based on improved particle swarm optimization algorithm.The problem of reactive power optimization, the key point is to deal with several problems: the various constraints on the processing, discrete regular processing, nonlinear problem processing and algorithm convergence results are reliable. After reading some of the relevant fields of reactive power optimization, this paper summarizes the characteristics of various types of algorithms, and according to their own research needs, to further determine the corresponding restrictions.According to the characteristics of reactive power optimization[3], three variables in reactive power optimization are processed by the hybrid encoding. The continuous variables are sorted into real numbers, and the discrete variables are sorted into integers. Reactive power optimization mathematical model for calculating the starting from the power system economy, point of application, the system chooses the active power loss minimum as objective function and state variables limit using penalty function into the objective function.Through the study and research of particle swarm algorithm and its improved algorithm, this paper designs two improvements on particle swarm algorithm. On the one hand is to proceed from the algorithm itself, the redesign of the algorithm of inertia weight and learning factor changes. On the other hand, through the integration of immune algorithm, particle swarm algorithm was improved from the outside[4]. Designed the immune particle swarm algorithm, this algorithm combines two algorithms advantages, eliminate the defects of single particle swarm algorithm has better global convergence.Finally, based on the MATLAB simulation platform, the SPSO and the IPSO are applied to the IEEE14 node and IEEE30 node test system, and the QPSO is compared. The simulation results for comparison analysis, three algorithms can obtain better optimization effect, effectively reduces the active power loss of the system, significantly enhance the voltage quality of the system, improve the distribution of reactive power. Ipso algorithm with respect to the QPSO and SPSO algorithm increases the more computing process and improvement strategies, in order to get rid of local extremum of deadlock, better search to a truly global optimal solution, by comparing proved the improved particle swarm optimization algorithm in power system reactive power optimization problem is effective and feasible.[5].