GPU-based power converter transient simulation with matrix exponential integration and memory management

Wu, Wei ORCID: https://orcid.org/0000-0001-7928-3602, Li, Peng, Fu, Xiaopeng, Wang, Zhiying, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Wang, Chengshan 2020. GPU-based power converter transient simulation with matrix exponential integration and memory management. International Journal of Electrical Power and Energy Systems 122 , 106186. 10.1016/j.ijepes.2020.106186

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Abstract

With the extensive application of power electronic equipment in power systems, electromagnetic transient (EMT) simulation involving power converters becomes more challenging. Due to its multithreads and high throughput architecture, the graphics processing unit (GPU) can be used to accelerate those EMT simulations. A GPU-based matrix exponential method and its memory management for power converter transient simulation are proposed in this paper. A parallel exponential integration algorithm is established from two aspects to fully utilize the GPU multithreads capability. The matrix exponentials which are recomputed with the on/off state changes of power electronic switches are cached in GPU memory. The simulation efficiency is improved by reusing the cached data and reducing heterogeneous data transmission between CPU and GPU. Several strategies are experimented to manage the cache memory considering the EMT simulation workflow. The proposed memory management expands the simulation capability by substantially reducing the memory requirement and maintains the speed advantage of the GPU-based simulator. The proposed method is tested on wind power plants of different scales with power electronic interfaced wind generators. Simulation results indicate that the proposed method and its memory management expand the simulation capability and achieve speedups.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0142-0615
Date of First Compliant Deposit:	2 October 2020
Date of Acceptance:	10 May 2020
Last Modified:	07 Nov 2023 06:31
URI:	https://orca.cardiff.ac.uk/id/eprint/135052

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