Parallel domain decomposition based distributed state estimation for large-scale power systems

Growing system sizes and complexity along with the large amount of data provided by phasor measurement units (PMUs) are the drivers to accurate state estimation algorithms for online monitoring and operation of power systems. In this paper a distributed weighted least square (WLS) state estimation method using additive Schwarz domain decomposition technique is proposed to reduce the computational execution time. The proposed approach divides the data set into several subsets to be processed in parallel using a multi-processor architecture considering data exchange among distributed areas. The slow coherency method and balanced partitioning are utilized to reduce the communication overhead and increase accuracy. Moreover, bad data analysis is also investigated in a distributed manner. The performance of the proposed distributed state estimator along with the speed-up for several test systems was compared with traditional centralized state estimator. The simulation results show a speed-up of 6.5 for a 4992-bus system.