GPU-Based Sparse Power Flow Studies With Modified Newton’s Method

The Power system is getting larger and more complicated due to development of multiple energy supplies. Solving large-scale power flow equations efficiently plays an essential role in analysis of power system and optimizing their performance during normal or contingencies operation. The traditional...

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Autores principales:	Lei Zeng, Shadi G. Alawneh, Seyed Ali Arefifar
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	GPU CUDA modified Newton’s method compressed row storage (CRS) Jacobian matrix vectorization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/bbaad01aa54d4201be6063bb86e3ab8e
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id	oai:doaj.org-article:bbaad01aa54d4201be6063bb86e3ab8e
record_format	dspace
spelling	oai:doaj.org-article:bbaad01aa54d4201be6063bb86e3ab8e2021-11-20T00:02:20ZGPU-Based Sparse Power Flow Studies With Modified Newton’s Method2169-353610.1109/ACCESS.2021.3127393https://doaj.org/article/bbaad01aa54d4201be6063bb86e3ab8e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9611282/https://doaj.org/toc/2169-3536The Power system is getting larger and more complicated due to development of multiple energy supplies. Solving large-scale power flow equations efficiently plays an essential role in analysis of power system and optimizing their performance during normal or contingencies operation. The traditional Newton-Raphson (NR) algorithm used for power flow calculations is computationally expensive due to updating Jacobian matrix in each iteration. As alternative to update the Jacobian matrix repeatedly, this paper presents a GPU-based sparse modified Newton’s method by the introduction of a fixed Jacobian matrix, which integrates vectorization and parallelization technique to accelerate power flow calculations. Moreover, this research in the paper also investigates the performance of the corresponding CPU versions and a MATLAB-based library package, MATPOWER. The comparison of the results on several power system and power distribution systems demonstrate that the GPU variant is more reliable and faster for power flow calculation in large-scale power systems.Lei ZengShadi G. AlawnehSeyed Ali ArefifarIEEEarticleGPUCUDAmodified Newton’s methodcompressed row storage (CRS)Jacobian matrixvectorizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153226-153239 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GPU CUDA modified Newton’s method compressed row storage (CRS) Jacobian matrix vectorization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	GPU CUDA modified Newton’s method compressed row storage (CRS) Jacobian matrix vectorization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Lei Zeng Shadi G. Alawneh Seyed Ali Arefifar GPU-Based Sparse Power Flow Studies With Modified Newton’s Method
description	The Power system is getting larger and more complicated due to development of multiple energy supplies. Solving large-scale power flow equations efficiently plays an essential role in analysis of power system and optimizing their performance during normal or contingencies operation. The traditional Newton-Raphson (NR) algorithm used for power flow calculations is computationally expensive due to updating Jacobian matrix in each iteration. As alternative to update the Jacobian matrix repeatedly, this paper presents a GPU-based sparse modified Newton’s method by the introduction of a fixed Jacobian matrix, which integrates vectorization and parallelization technique to accelerate power flow calculations. Moreover, this research in the paper also investigates the performance of the corresponding CPU versions and a MATLAB-based library package, MATPOWER. The comparison of the results on several power system and power distribution systems demonstrate that the GPU variant is more reliable and faster for power flow calculation in large-scale power systems.
format	article
author	Lei Zeng Shadi G. Alawneh Seyed Ali Arefifar
author_facet	Lei Zeng Shadi G. Alawneh Seyed Ali Arefifar
author_sort	Lei Zeng
title	GPU-Based Sparse Power Flow Studies With Modified Newton’s Method
title_short	GPU-Based Sparse Power Flow Studies With Modified Newton’s Method
title_full	GPU-Based Sparse Power Flow Studies With Modified Newton’s Method
title_fullStr	GPU-Based Sparse Power Flow Studies With Modified Newton’s Method
title_full_unstemmed	GPU-Based Sparse Power Flow Studies With Modified Newton’s Method
title_sort	gpu-based sparse power flow studies with modified newton’s method
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/bbaad01aa54d4201be6063bb86e3ab8e
work_keys_str_mv	AT leizeng gpubasedsparsepowerflowstudieswithmodifiednewtonx2019smethod AT shadigalawneh gpubasedsparsepowerflowstudieswithmodifiednewtonx2019smethod AT seyedaliarefifar gpubasedsparsepowerflowstudieswithmodifiednewtonx2019smethod
_version_	1718419860816396288

GPU-Based Sparse Power Flow Studies With Modified Newton&#x2019;s Method

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