A Hierarchical Low-Rank Approximation Based Network Solver for EMT Simulation

In electromagnetic transient (EMT) simulation, 80-97% of the computational time is devoted to solving the network equations. A key observation is that the sub-matrix representing the interaction between two far-away groups of buses is usually sparse and can be approximated by a low-rank matrix. Base...

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Vydáno v:IEEE transactions on power delivery Ročník 36; číslo 1; s. 280 - 288
Hlavní autoři: Zhang, Lu, Wang, Bin, Zheng, Xiangtian, Shi, Weiping, Kumar, P. R., Xie, Le
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Approximation
Computational modeling
Computing time
Electromagnetic transient (EMT)
Generators
graphic partition
hierarchical low-rank approximation
Integrated circuit modeling
Load modeling
Mathematical analysis
Mathematical model
Matrix algebra
Matrix methods
Multiplication
network solution
Numerical models
Parallel processing
Simulation
Transmission line matrix methods
ISSN:0885-8977, 1937-4208
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Shrnutí:In electromagnetic transient (EMT) simulation, 80-97% of the computational time is devoted to solving the network equations. A key observation is that the sub-matrix representing the interaction between two far-away groups of buses is usually sparse and can be approximated by a low-rank matrix. Based on this observation, we propose a novel low-rank approximation method which permits <inline-formula><tex-math notation="LaTeX">{O(N \log N)}</tex-math></inline-formula>-time matrix-vector multiplication for each network solution time step. Comprehensive numerical studies are conducted on a 39-bus system and a 179-bus system from the literature, and large cases created from the two systems. The results demonstrate that the proposed approach is up to <inline-formula><tex-math notation="LaTeX">2.8\times</tex-math></inline-formula> faster than the state-of-the-art sparse LU factorization based network solution, without compromising simulation accuracy. Since our low-rank approximation is highly parallelizable, further speedup may be possible.
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ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2020.2978128