Applications of data mining technique for power system transient stability prediction

This paper presents a data mining framework for the historical data of measurement and simulation units. Taking example for transient stability prediction, this paper establishes a data mining flow. The data market of transient stability is built up by all kinds of data sources. The data market is convenient for online analytical processing. At the same time, many model of data mining can be constructed based on the data market. We can acquire more knowledge of the power system transient stability. The IEEE 39-Bus test system is employed to demonstrate the validity of the proposed approach.

[1]  Boonserm Kijsirikul,et al.  Multiclass support vector machines using adaptive directed acyclic graph , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[3]  K. R. Padiyar,et al.  Transient stability assessment using artificial neural networks , 2000, Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482).

[4]  Hyun-Chul Kim,et al.  Pattern classification using support vector machine ensemble , 2002, Object recognition supported by user interaction for service robots.

[5]  F. Aboytes,et al.  TRANSIENT STABILITY ASSESSMENT IN LONGITIJDINAL POWER SYSTEMS USING ARTIFICIAL NEURAL NETWORKS , 1996 .

[6]  S. Gunn Support Vector Machines for Classification and Regression , 1998 .