Power System State Optimization Estimation Based On Particle Swarm Optimization

Grid measurements are usually sent to the control center through a specific communication protocol.Communication errors,instrument failures,incomplete measurements,and unavailability of measurements can create uncertainties in measurements,which in turn lead to instability in the power system state estimation.Therefore,in order to ensure the safety and stable operation of the power system and grasp the power system operation status in time,it is of great significance to estimate the state of the power system.This thesis first introduces the concept of power system state estimation,the necessity of state estimation,the function of state estimation,the use of state estimation,the network equation of power system and the steps of state estimation.Secondly,the power system is modeled by weighted least square method and weighted least absolute value method,and state estimation is performed.The principle of the standard particle swarm algorithm was introduced again,and the parameters after the particle swarm optimization were analyzed.After the particle swarm algorithm was compared with other algorithms,the premature phenomenon was improved.Finally,PSO and improved Particle Swarm Optimization(MPSO)algorithm are applied to standard data IEEE 5-node and 30-node test systems for active and reactive power optimization.A comparative study of the weighted least square(WLS)method and the weighted least absolute value(WLAV)method based on traditional particle swarm optimization(PSO).Use MATLAB software for simulation.The comparison results show that the MPSO algorithm converges faster than the basic PSO algorithm.