A Power Flow Algorithm with Three-order Convergence rate
被引:0
|
作者:
Sun Yingyun
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机构:
Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R ChinaTsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
Sun Yingyun
[1
]
Liu Dong
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机构:
Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R ChinaTsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
Liu Dong
[1
]
He Guangyu
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机构:
Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R ChinaTsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
He Guangyu
[1
]
Mei Shengwei
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h-index: 0
机构:
Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R ChinaTsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
Mei Shengwei
[1
]
机构:
[1] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
power flow calculation;
current injected model;
three-order convergence;
D O I:
暂无
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
A power flow algorithm with three-order convergence is proposed, which make full use of the second order derivative information of power flow equations, and it can decrease the iterations effectively. To reduce calculation burden of Hession matrix, A new power flow model is given in the paper. Both node voltages and injected currents are treated as variables. Traditional power flow equations are departed to linear network equations and nonlinear node equations. The Hession matrix of the nonlinear equations is const matrix with simple structure. Simulation results of IEEE test cases and several real systems show that the proposed method can converge after only 2 similar to 3 iterations with fast speed.
机构:
Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R ChinaShenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
Zhao, Licheng
Pu, Wenqiang
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h-index: 0
机构:
Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R ChinaShenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
Pu, Wenqiang
Zhou, Rui
论文数: 0引用数: 0
h-index: 0
机构:
Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R ChinaShenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
Zhou, Rui
Shi, Qingjiang
论文数: 0引用数: 0
h-index: 0
机构:
Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China
Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R ChinaShenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
机构:
Nanjing Normal Univ, Sch Math & Comp Sci, Inst Math, Nanjing 210097, Jiangsu, Peoples R China
Changchun Normal Univ, Coll Math, Changchun 130032, Jilin, Peoples R ChinaNanjing Normal Univ, Sch Math & Comp Sci, Inst Math, Nanjing 210097, Jiangsu, Peoples R China
Liang, Sihua
Zhang, Jihui
论文数: 0引用数: 0
h-index: 0
机构:
Nanjing Normal Univ, Sch Math & Comp Sci, Inst Math, Nanjing 210097, Jiangsu, Peoples R ChinaNanjing Normal Univ, Sch Math & Comp Sci, Inst Math, Nanjing 210097, Jiangsu, Peoples R China