DocumentCode
2596534
Title
Study on decomposition and coordination methods for voltage stability assessment of large interconnected power system
Author
Zhao, J. ; Shi, F. ; Chen, G.
Author_Institution
Dept. of Electr. Eng., Hohai Univ., Nanjing, China
fYear
2009
fDate
6-7 April 2009
Firstpage
1
Lastpage
6
Abstract
It is imperative to accurately consider the effect of external network on internal system´s voltage stability for Online voltage stability assessment of a subsystem within a large interconnected power grid. Since the heaviest load condition of a system and a set of credible severe contingencies have to be considered in VSA, traditional network equivalent with respect to a snapshot case cannot meet the requirements. Two different distributed computation modes are proposed to exactly consider the effects of external networks. They are the synchronous iteration (SI) mode and the asynchronous iteration (AI) mode. The continuation power flow methods under both SI and AI mode are developed to calculate the load margins. The numerical results of IEEE118 bus power system indicate that the decomposition and coordination methods can get the better results comparing to the local equivalence methods.
Keywords
decomposition; iterative methods; power grids; power system interconnection; IEEE118 bus; coordination methods; decomposition methods; distributed computation modes; interconnected power system; network equivalent; power grid; synchronous iteration; voltage stability assessment; Artificial intelligence; Computer networks; Distributed computing; Grid computing; Load flow; Power grids; Power system interconnection; Power system modeling; Power system stability; Voltage; Asynchronous Iteration Mode; Continuation Power Flow; Distributed Computation; Synchronous Iteration Mode; Voltage Stability Assessment;
fLanguage
English
Publisher
ieee
Conference_Titel
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4934-7
Type
conf
DOI
10.1109/SUPERGEN.2009.5347880
Filename
5347880
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