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Contingency filtering technique for transient stability constrained optimal power flow
Author(s) -
Jiang Quanyuan,
Huang Zhiguang,
Xu Kai
Publication year - 2013
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2013.0072
Subject(s) - power flow , transient (computer programming) , contingency , stability (learning theory) , control theory (sociology) , computer science , electric power system , mathematical optimization , flow (mathematics) , power (physics) , mathematics , artificial intelligence , physics , machine learning , linguistics , philosophy , control (management) , quantum mechanics , operating system , geometry
Transient stability constrained optimal power flow (TSCOPF) is an important and difficult problem. When multiple contingencies are considered, a reliable contingency filtering technique should be used to reduce the scale of TSCOPF problem. This study brings in the concepts of active contingency and critical contingency, and develops a novel contingency filtering strategy. Based on time‐domain numerical simulations, the proposed contingency filtering strategy first screens all the considered contingencies and identifies active contingencies whose severe indices violate the pre‐defined threshold of transient stability, then further finds out the critical contingencies in which some generators are most severely disturbed according to the severe indices trajectories. The severe indices can be such as maximal relative rotor angles, maximal transient generator voltage dips and so on. Taking only the critical contingencies into account, the scale of TSCOPF problem is reduced significantly. Interior point method is used to solve the reduced TSCOPF problem. Numerical results on several cases indicate that the proposed contingency filtering technique is reliable and efficient. Compared with the conventional TSCOPF approach, which involves all the contingencies, the proposed contingency filtering strategy possesses overwhelming advantages in CPU time and memory consumption, and is hopeful to solve TSCOPF problems with many contingencies.

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