Conjugate Gradient Approach to Parallel Processing in Dynamic Simulation of Power Systems

Pai, M.A.; Sauer, Peter W.; Kulkarni, A.

doi:10.23919/acc.1992.4792388

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The goal for achieving practical realtime parallel dynamic simulations was already being set in the 90s [211]. Pioneering applications used the Conjugate Gradient method on the Cray Y-MP, iPSC/860, and IBM 3090 mainframe [212] [213]. Parallel dynamic simulation studies quickly moved to large system implementations emphasizing balanced network partitioning and computational load and creating parallel software tools in the 2000s [214].…”

Section: Developmentmentioning

confidence: 99%

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Al-Shafei¹,

Zareipour²,

Cao³

2022

Preprint

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Section: Developmentmentioning

confidence: 99%

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Al-Shafei¹,

Zareipour²,

Cao³

2022

Preprint

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“…Alternatively, the approaches in [8,9,11,16] decouple the quasisteady-state network equations from the differential equations.…”

Section: Introductionmentioning

confidence: 99%

Convergence analysis of a parallel newton scheme for dynamic power grid simulations

Robbins

Zavala

2011

Proceedings of the First International Workshop on High Performance Computing, Networking and Analytics for the Power Grid

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We analyze the convergence properties of a parallel Newton scheme for differential systems. The scheme concurrently solves the time-coupled nonlinear systems arising from the application of implicit discretization schemes. We have found that the scheme acts as a tracking algorithm that converges to a moving manifold given by the solution of the nonlinear system at the current time step parameterized in the iterating solution of the previous step. This property explains why the method can significantly reduce the number of iterations compared with the sequential Newton method that marches forward in time. The method exhibits a theoretical lower bound on the number of iterations equal to the number of discretization points. A numerical study using a detailed dynamic power grid model is provided to demonstrate the scalability of the method.

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“…First, from the perspective of parallel algorithm, CG is more computationally efficient for large‐scale systems because CG has less data dependency and requires less intermediate memory storage [14]. Second, from the perspective of parallel implementation, the development of parallel hardware architecture such as GPU can efficiently accommodate the CG algorithm [26–30].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the largest eigenvalue in Chebyshev preconditioner for parallel conjugate gradient method‐based power flow computation

Li¹,

Li²

2016

IET gener. transm. distrib.

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Conjugate Gradient Approach to Parallel Processing in Dynamic Simulation of Power Systems

Cited by 10 publications

References 11 publications

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Convergence analysis of a parallel newton scheme for dynamic power grid simulations

Estimation of the largest eigenvalue in Chebyshev preconditioner for parallel conjugate gradient method‐based power flow computation

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