Automatic learning of fine operating rules for online power system security control

[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 :

Issue :

Pages :

1708-1719

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 17 January 2016

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