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Massively Parallel Electromagnetic Transient Simulation of Large Power Systems

Authors :
Zhou, Zhiyin
Publication Year :
2018

Abstract

Abstract: Electromagnetic transient (EMT) simulation, which is widely utilized in power system planning and design, is one of the most complex power system studies that requires detailed modeling of the study system including all frequency-dependent and nonlinear effects. Large-scale EMT simulation is becoming commonplace due to the increasing growth and interconnection of power grids, and the need to study the impact of system events of the wide area network. To cope with enormous computational burden, the massively parallel architecture of the graphics processing unit (GPU) is exploited in this work for large-scale EMT simulation. A fine-grained network decomposition, called shattering network decomposition, is proposed to divide the power system network exploiting its topological and physical characteristics into linear and nonlinear networks, which adapt to the unique features of the GPU-based massive thread computing system. Large-scale systems, up to 240,000 nodes, with typical components, including synchronous machines, transformers, transmission lines and nonlinear elements, are tested and compared with mainstream simulation software to verify the accuracy and demonstrate the speed-up improvement with respect to sequential computation. Power electronic devices are widely utilized in modern power grid, especially for AC/DC converters in HVDC systems. The proposed fine-grained decomposition algorithm can also be applied in the simulation of multiple levels modular multilevel converter (MMC) consisting of Insulated Gate Bipolar Transistors (IGBTs) based on linear and nonlinear switch models, which effectively enhances the simulation performance and extends the system scale by parallelizing the calculation and maintaining the convergence during the computation.

Details

Language :
English
Database :
OpenDissertations
Publication Type :
Dissertation/ Thesis
Accession number :
ddu.oai.era.library.ualberta.ca.2d481cf2.0f15.4f73.b0d1.ba7034f50305