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The electromagnetic transient (EMT) simulation of a arge-scale power system consumes so much computational power that parallel programming techniques are urgently needed in this area. For example, realistic-sized power systems include thousands of buses, generators, and transmission lines. Massive-thread 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 CPU, the graphic-processing unit (GPU) has many more cores with distributed memory which can offer higher data throughput. This paper proposes a massive-thread EMT program (MT-EMTP) and develops massive thread parallel modules for linear passive elements, the universal line model, and the universal machine model for offline EMT simulation. An efficient node-mapping structure is proposed to transform the original power system admittance matrix into a block-node diagonal sparse format to exploit the massive thread parallel GPU architecture. The developed MT-EMTP program has been tested on large-scale power systems of up to 2458 three-phase buses with detailed component modeling. The simulation results and execution times are compared with mainstream commercial software, EMTP-RV, to show the improvement in performance with equivalent accuracy.
parallel programming, power system simulation, massive-thread, parallel algorithms, graphics processors, Electromagnetic transient analysis
parallel programming, power system simulation, massive-thread, parallel algorithms, graphics processors, Electromagnetic transient analysis
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 66 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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