POWER SYSTEM STATE ESTIMATION BY THE COMBINASION OF GENETIC ALGORITHM, PARTICLE SWARM OPTIMIZATION AND DECOUPLED VARIABLES

Abstract

State estimation tools is an indispensable part in the process of implementing power system monitoring and control, and is an important part of the smart grid implementation process. In particular, the estimated value of the state variable helps the operator make control decisions when parameters exceed allowable limits with the goal of ensuring safe and reliable system operation. In this paper, the combination of genetic algorithm, swarm optimization algorithm and variable separation technique is applied to solve the problem of estimating state variable values for power transmission grids. The process of estimating the voltage module and voltage phase angle is performed through the swarm optimization algorithm and the genetic algorithm, respectively. The proposed method is validated by calculations on sample IEEE 14, 30 and 118 node power grids. The estimated results of voltage magnitude and phase angle are approximated to the reference values.