PROJECT TITLE :
Parallel Domain-Decomposition-Based Distributed State Estimation for Large-Scale Power Systems
Growing system sizes and complexity, together with the large amount of information provided by phasor measurement units (PMUs), are the drivers to accurate state estimation algorithms for on-line monitoring and operation of Power Systems. In this paper, a distributed weighted-least-sq. state estimation technique using an additive Schwarz domain decomposition technique is proposed to cut back the computational execution time. The proposed approach divides a information set into many subsets to be processed in parallel employing a multiprocessor design considering information exchange among distributed areas. The slow coherency technique and balanced partitioning are utilised to reduce the Communication overhead and increase accuracy. Moreover, dangerous information analysis is also investigated in a distributed manner. The performance of the proposed distributed state estimator, together with the speed-up for several check systems, was compared with the traditional centralized state estimator. The simulation results show a speed-of half dozen.5 for a 4992-bus system.
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