DocumentCode :
1248656
Title :
Security boundary visualization for systems operation
Author :
McCalley, James D. ; Wang, Shimo ; Zhao, Qianglin ; Zhou, Guozhong ; Treinen, Roger T. ; Papalexopoulos, Alex D.
Author_Institution :
Iowa State Univ., Ames, IA, USA
Volume :
12
Issue :
2
fYear :
1997
fDate :
5/1/1997 12:00:00 AM
Firstpage :
940
Lastpage :
947
Abstract :
This paper presents a security assessment approach for operational planning studies that provides the operator with accurate boundary visualization in terms of easily monitored precontingency information. The approach is modeled after traditional security assessment procedures which result in use of a nomogram for characterizing the security boundaries; these procedures are common among many North American utilities today. Therefore, the approach builds on what is already familiar in the industry, but it takes advantage of computer automation and neural networks for generating and understanding large databases. The appeal of the approach is threefold: it provides increased accuracy in boundary representation, it reduces the labor traditionally required in generating security boundaries, and the resulting boundaries, encoded in fast, flexible C subroutines, can be integrated into energy management system software to provide the operator with compact, understandable boundary illustration in real time. These improvements are of particular interest in securely operating transmission systems close to their limits so as to fully utilize existing facilities
Keywords :
load management; neural nets; power system analysis computing; power system planning; power system security; North American utilities; boundary visualization; computer automation; energy management system software; flexible C subroutines; neural networks; operational planning studies; precontingency information; security assessment; security boundary visualization; systems operation; Algorithms; Automation; Computer industry; Computer networks; Data security; Databases; Information security; Monitoring; Neural networks; Visualization;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/59.589783
Filename :
589783
Link To Document :
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