Abstract:In order to solve the existing problems in reactive power optimization power flow calculation of power grid system, such as the large amount of calculation, the voltage values of each node in the calculation result may lead to the output of reactive power source approaching the limit value, and may conflict with the system voltage safety, and the output of generator exceeding the limit, etc. In this paper, an improved particle swarm optimization algorithm with penalty function, learning factor and inertia weight is used to solve the reactive power flow calculation problem of two typical systems (5-node typical system and 39-node typical system) under given constraints. Through the analysis and comparison of the calculation results, this paper summarizes how to deal with the constraints in the power grid and how to set the parameters and ranges in the particle swarm optimization. The influence of grid constraints on reactive power optimization results is discussed. The setting principles of penalty function, inertia weight and learning factor in particle swarm optimization (PSO) and their effects on the convergence of PSO are given. The improvement of PSO is prospected.