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Graph Computing based Fast Screening in Contingency Analysis
During last decades, contingency analysis has been facing challenges fro...
03/29/2019 ∙ by Yiting Zhao, et al. ∙
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Graph Based Power Flow Calculation for Energy Management System
Power flow calculation in EMS is required to accommodate a large and com...
10/25/2018 ∙ by Junjie Shi, et al. ∙
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Graph Computing based Distributed Fast Decoupled Power Flow Analysis
Power flow analysis plays a fundamental and critical role in the energy ...
02/19/2019 ∙ by Chen Yuan, et al. ∙
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Real-Time Contingency Analysis with Corrective Transmission Switching - Part I: Methodology
Transmission switching (TS) has gained significant attention recently. H...
04/15/2016 ∙ by Xingpeng Li, et al. ∙
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A High-Performance Energy Management System based on Evolving Graph
As the fast growth and large integration of distributed generation, rene...
04/08/2019 ∙ by Guangyi Liu, et al. ∙
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Graph Computing based Distributed State Estimation with PMUs
Power system state estimation plays a fundamental and critical role in t...
02/20/2020 ∙ by Yi Lu, et al. ∙
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Contingency Analysis Based on Partitioned and Parallel Holomorphic Embedding
In the steady-state contingency analysis, the traditional Newton-Raphson...
04/07/2021 ∙ by Rui Yao, et al. ∙
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Graph-based Preconditioning Conjugate Gradient Algorithm for N-1 Contingency Analysis
03/08/2018 ∙ by Yiting Zhao, et al. ∙
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Contingency analysis (CA) plays a critical role to guarantee operation security in the modern power systems. With the high penetration of renewable energy, a real-time and comprehensive N-1 CA is needed as a power system analysis tool to ensure system security. In this paper, a graph-based preconditioning conjugate gradient (GPCG) approach is proposed for the nodal parallel computing in N-1 CA. To pursue a higher performance in the practical application, the coefficient matrix of the base case is used as the incomplete LU (ILU) preconditioner for each N-1 scenario. Additionally, the re-dispatch strategy is employed to handle the islanding issues in CA. Finally, computation performance of the proposed GPCG approach is tested on a real provincial system in China.
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