Open AccessRoot event‐based dynamic space pruning algorithm for contingency screening of transmission system
Author(s) -
Wang Leibao,
Hu Bo,
Xie Kaigui,
Niu Tao,
Li Chunyan,
Li Wenyuan,
Liao Qinglong,
Wan Lingyun,
Zhang Ying
Publication year - 2020
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.2019.1804
Subject(s) - subspace topology , pruning , event (particle physics) , computer science , event tree , algorithm , identification (biology) , dynamic programming , bilevel optimization , complement (music) , transmission (telecommunications) , data mining , mathematical optimization , mathematics , artificial intelligence , reliability engineering , optimization problem , engineering , fault tree analysis , telecommunications , quantum mechanics , physics , botany , biology , chemistry , biochemistry , agronomy , complementation , gene , phenotype
Contingency screening is vital for maintaining the transmission system reliability. Bilevel programming is a powerful tool for the accurate identification of critical events. To accelerate the contingency screening with bilevel programming, this study proposes a root‐event‐based dynamic space pruning algorithm. A root event is an event in which all the failed components have contributions to the event impact. Considering the fact that numerous N − k events describe the same critical scenario, the proposed space pruning principles partition the N − k event space into the subspace of root events and the complement subspace. To accelerate the screening of subspace of root events, bilevel programming is constructed to identify new critical events having high risk and causing more severe load shedding than the root event. Besides, relaxed bilevel programming is proposed to accelerate the screening of the complement subspace. The relaxed scheme replaces the constraints of power flow by constraints of transmission capacity, and enables the identification of most of the critical events in the complement subspace with a low computational cost. Case studies on the IEEE RTS and a provincial grid in China indicate that the proposed algorithm significantly accelerates the screening process without missing critical events.