Article (Scientific journals)
Automatic learning of fine operating rules for online power system security control
Sun, Hongbin; Zhao, Feng; Wang, Huifang et al.
2016In IEEE Transactions on Neural Networks and Learning Systems, 27 (8), p. 1708-1719
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Keywords :
Machine learning; Power systems security; Reliability control
Abstract :
[en] Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.
Disciplines :
Computer science
Energy
Electrical & electronics engineering
Author, co-author :
Sun, Hongbin
Zhao, Feng
Wang, Huifang
Wang, Kangping
Jiang, Wei
Guo, Qinglai
Zhang, Boming
Wehenkel, Louis  ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Automatic learning of fine operating rules for online power system security control
Publication date :
August 2016
Journal title :
IEEE Transactions on Neural Networks and Learning Systems
ISSN :
2162-237X
eISSN :
2162-2388
Publisher :
IEEE
Volume :
27
Issue :
8
Pages :
1708-1719
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 17 January 2016

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