Large-Scale Transient Stability Simulation of Electrical Power Systems on Parallel GPUs

Jalili-Marandi, Vahid; Zhou, Zhiyin; Dinavahi, Venkata

doi:10.1109/tpds.2011.291

Cited by 74 publications

(26 citation statements)

References 33 publications

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“…Following, several methods were proposed inspired by different hardware platforms, memory models and partitioning schemes [2], [19], [20], [21]. Some recently proposed methods make use of both coarse-grained and fine-grained parallelization in a nested way [1] to increase performance. Several characteristics differentiate these methods.…”

Section: B3 Coarse-grained Parallel Methodsmentioning

confidence: 99%

Dynamic Simulation of Large-Scale Power Systems Using a Parallel Schur-Complement-Based Decomposition Method

Aristidou

Fabozzi

Cutsem

2014

IEEE Trans. Parallel Distrib. Syst.

108

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Article:Aristidou, P orcid.org/0000-0003- 4429-0225, Fabozzi, D and Van Cutsem, T (2014) Dynamic simulation of large-scale power systems using a parallel schur-complement-based decomposition method. IEEE Transactions on Parallel and Distributed Systems, 25 (10). pp. 2561 -2570 . ISSN 1045 https://doi.org/10.1109/TPDS.2013.252 © 2013 IEEE. This is an author produced version of a paper published in IEEE Transactions on Parallel and Distributed Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. APPENDIX A DOMAIN DECOMPOSITION METHODSDDMs were originally used due to the lack of memory in computing systems: data needed for smaller portions of a problem could fit entirely to the memory while for the whole problem they could not. They lost their appeal as larger and cheaper memory became available, only to resurface in the era of parallel computing. These methods are inherently suited for execution on parallel architectures and many parallel implementations have been presented on multi-core computers, clusters and lately Graphics Processing Units (GPUs) [1], [2]. They are mainly distinguished by three features: subdomain partitioning, problem solution over sub-domains and sub-domain interface variable processing [3]. A.1 Sub-domain PartitioningSub-domain partitioning has to be chosen based on the desired sub-domain characteristics for the given problem. This includes choosing the number of sub-domains, the type of partitioning, and the level of overlap between the sub-domains. Each of these choices depend on a variety of factors such as size, type, and geometry of the problem domain, the number of parallel processors, communication cost, and the actual system being solved.When considering spatial domain problems, such as PDEs, the decomposition is usually g...

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Section: B3 Coarse-grained Parallel Methodsmentioning

confidence: 99%

Dynamic Simulation of Large-Scale Power Systems Using a Parallel Schur-Complement-Based Decomposition Method

Aristidou

Fabozzi

Cutsem

2014

IEEE Trans. Parallel Distrib. Syst.

108

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“…In contrast, the testing platform for CPU algorithm is a notebook with an Intel Core i7-3520M 2.90 GHz CPU and 4 GB memory. The CPU algorithm is based on the optimized model (8) and (9), and the CPU baseline, a single-threaded program, is implemented in house by C++. In addition, the GPU and CPU algorithms are all implemented based on single-precision floatingpoint format, and are working on Windows 7.…”

Section: Cpumentioning

confidence: 99%

“…In order to reduce the SLS solving time, Refs. [4][5][6][7][8][9][10] studied the GPU-accelerated strategies for common SLS solving algorithms, such as LU factorization, conjugate gradient (CG) iteration method, Jacobi iteration and multifrontal method. In those literatures, 3-10 times speedup has been reported.…”

Section: Introductionmentioning

confidence: 99%

A novel GPU-accelerated strategy for contingency screening of static security analysis

Zhou

Zhang

Bo³

et al. 2016

International Journal of Electrical Power & Energy Systems

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“…In [16]- [18], GPUs are used to simulate power system transient-stability scenarios. The transient-stability is evaluated by discretizing the differential equations describing the system dynamics.…”

Section: Existing Power System Applicationsmentioning

confidence: 99%

GPU-Accelerated Solutions to Optimal Power Flow Problems

Rakai

Rosehart

2014

2014 47th Hawaii International Conference on System Sciences

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The optimal power flow problem (OPF) has been of importance to power system operators for many decades. Being able to quickly determine optimal operating points and analyzing larger networks can lead to advantages for operators from reliability, stability, cost and market fairness perspectives. This work aims at achieving those ends by solving OPF problems by utilizing hardware acceleration capabilities of graphical processing units (GPUs). At present, nearly all desktop and laptop computers ship with general-purpose GPUs that can be harnessed to accelerate analysis. This work will present important concepts regarding effective use of GPUs as it pertains to OPF problems and illustrate the types of problems that stand to benefit most from their use. The benefits of GPU acceleration are demonstrated by implementing a predictor-corrector interior-point method with the majority of the computation offloaded onto a GPU. Experiments are used to validate the developments by analyzing well-known power systems.

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Large-Scale Transient Stability Simulation of Electrical Power Systems on Parallel GPUs

Cited by 74 publications

References 33 publications

Dynamic Simulation of Large-Scale Power Systems Using a Parallel Schur-Complement-Based Decomposition Method

Dynamic Simulation of Large-Scale Power Systems Using a Parallel Schur-Complement-Based Decomposition Method

A novel GPU-accelerated strategy for contingency screening of static security analysis

GPU-Accelerated Solutions to Optimal Power Flow Problems

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