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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.

Abstract: Electromagnetic transient (EMT) simulation is widely utilized in power system planning and design. Respecting the detail and complexity of the components models, the electromagnetic transient program (EMTP) demands significant computational power. Increasing with the scale of the system, this requirement has become so prominent that parallel programming techniques are urgently needed in large-scale power system EMT simulation. Improving upon the multithreading parallelism, massive-threading computing is one of the key developments that can increase the EMT computational capabilities substantially when the processing unit has enough hardware cores. Compared to the traditional central processing unit (CPU), the graphic processing unit (GPU) has many more cores with distributed memory which can offer higher data throughput. This thesis describes the conception of the massive-threading parallel EMTP based on GPU for large-scale power systems using compute unified device architecture (CUDA). It defines a new fundamental program framework, relevant basic data structures and efficient data interfaces of the massive-threading parallel EMT simulator. The thesis proposes a series of massive-threading parallel modules for component models including unified linear passive elements module (ULPEM), universal line module (ULM) and universal machine module (UMM); and numerical methods, including Newton-Raphson iteration module (NRIM) and forward-backward substitution with LU factorization module (FBSLUM), used in the EMTP. Without the need for a trade-off between the system scale and the complexity of the component models, all parallel modules proposed above are detailed, universal and unified. In order to fully release the computing power of modern computer system, both data and instructions are based on 64-bit architecture, which guarantee the precision as well as extensibility of the program. The developed MT-EMTP program has been tested on various large-scale power systems of up to 2458 three-phase buses with detailed component modeling. The simulation results and execution times are compared with a mainstream commercial software, EMTPRV r, to show the improvement in performance with equivalent accuracy.