Open AccessQuick and effective multiple contingency screening algorithm based on long‐tailed distribution
Author(s) -
Long Cheng,
You Dahai,
Hu Jin,
Wang Gang,
Dong Meiling
Publication year - 2016
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.2015.0885
Subject(s) - contingency table , computer science , contingency , algorithm , data mining , index (typography) , basis (linear algebra) , feature (linguistics) , core (optical fiber) , electric power system , power (physics) , machine learning , mathematics , telecommunications , philosophy , linguistics , geometry , physics , quantum mechanics , world wide web
This study proposes a quick and effective algorithm for screening severe N − 2 contingency, thus avoiding exhausting analysis of all conceivable N − 2 contingencies. First, the index of overload contribution rate (OCR) is developed on the basis of distribution factor, which is discovered to be long‐tailed distributed by statistics. Then, by fully exploiting the discovered feature, the so‐called technique of combination of partitioned data sets (TCPDS) is designed and described. The core of the contingency screening algorithm is to apply the TCPDS to the OCR. Moreover, an index system is established to evaluate the proposed algorithm. Finally, the proposed algorithm is proved to be effective and efficient by the index system when applied to some practical power systems.