Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

Yan Xu; Xin Chen

doi:10.4028/www.scientific.net/AMM.521.418

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 521Rules Mining of Power System Transient Stability...

Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

Abstract:

Transient Stability Assessment (TSA) aims at assessing stability of power system operation state quickly. This paper introduces rough set theory and clustering analysis to assess power system transient stability. At first, the stability operation parameters and fault places are taken as feature attributes based on the trait of power system transient ability. K-means algorithm is used to make continuous attributes among feature attributes discrete. Then feature attributes and stability types are taken as conditional attributes and decision attributes respectively. Initial decision table is established. Finally, rough set theory is used to form final decision table and rules of TSA are obtained. The IEEE 9-Bus system is employed to demonstrate the validity of the proposed approach.

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Periodical:

Applied Mechanics and Materials (Volume 521)

Pages:

418-422

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.521.418

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Online since:

February 2014

Authors:

Yan Xu, Xin Chen*

Keywords:

Attribute Reduction, K-Means Algorithm, Power System, Rough Set (RS), Transient Stability

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References

[1] Xiaotong Tong and Shengyong Ye: Power System Technology. Vol. 33 (2009), pp.88-93.

Google Scholar

[2] Pang-Ning Tan, Michael Steinbach and Vipin Kumar: Introduction to Data Mining (Pearson Education, USA 2006).

Google Scholar

[3] Chuanjian Yang, Hao Ge and Zhisheng Wang: Application Research of Computers. Vol. 29 (2006), pp.16-20.

Google Scholar

[4] Dan Pan and Qilun Zheng: Journal of Computer Research & Development. Vol. 38 (2012), pp.904-910.

Google Scholar

[5] Guoyin Wang: Rough Set Theory and Knowledge Acquisition (Xi'an Jiaotong University Press, China 2001).

Google Scholar

[6] Kerber R C: Proceedings of the 10th National Conference in Artificial Intelligence. (MIT Press, USA 1992).

Google Scholar

[7] Chenshan Wang, Jing Cao and Guangyuan Chen: Power System Technology. Vol. 29 (2005), pp.18-22.

Google Scholar

[8] Xiaoxia Huang and Yunshi Xiao: Application Research of Computers. Vol. 5 (2003), pp.37-39.

Google Scholar

[9] Yi Fang, Jun Wang and Xiaoru Wang: Mechanical Engineering. Vol. 23 (2007), pp.51-54.

Google Scholar