Automatic Learning of Fine Operating Rules for Online Power System Security Control

作者：Sun, Hongbin^*; Zhao, Feng; Wang, Hao; Wang, Kang; Jiang, Weiyong; Guo, Qinglai; Zhang, Boming; Wehenkel, Louis

来源：IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(8): 1708-1719.

DOI：10.1109/TNNLS.2015.2390621

• Automatic learning
• critical flowgate
• knowledge discovery
• online security analysis
• smart grid
• total transfer capability

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.

• 出版日期2016-8
• 单位清华大学