Improved Approximate Minimum Degree Ordering Method and Its Application for Electrical Power Network Analysis and Computation

• 作者: Jian Guo Hong Liang Songpu Ai Chao Lu Haochen Hua Junwei Cao
• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2021,（4）
• 页码范围: 464-474
• 页数: 11页
• 语种: 英文
• DOI: 10.26599/TST.2020.9010019

摘要

Electrical power network analysis and computation play an important role in the planning and operation of the power grid,and they are modeled mathematically as differential equations and network algebraic equations.The direct method based on Gaussian elimination theory can obtain analytical results.Two factors affect computing efficiency:the number of nonzero element fillings and the length of elimination tree.This article constructs mapping correspondence between eliminated tree nodes and quotient graph nodes through graph and quotient graph theories.The Approximate Minimum Degree(AMD)of quotient graph nodes and the length of the elimination tree nodes are composed to build an Approximate Minimum Degree and Minimum Length(AMDML)model.The quotient graph node with the minimum degree,which is also the minimum length of elimination tree node,is selected as the next ordering vector.Compared with AMD ordering method and other common methods,the proposed method further reduces the length of elimination tree without increasing the number of nonzero fillings;the length was decreased by about 10％compared with the AMD method.A testbed for experiment was built.The efficiency of the proposed method was evaluated based on different sizes of coefficient matrices of power flow cases.