Abstract
With the rapid development of power grid, the operational and operational characteristics of large scale power grid have become increasingly variable and complex, which greatly increases the operational risks of power grid. In this paper, an novel distributed security feature selection algorithm based on K-means clustering is proposed, which aims to offer effective information for power system security and stability. First, all the security features are collected and then clustered into several groups using K-means algorithm, which are distributed to different nodes. Then, the key features is selected and used to establish a fine operational rule, and helps operator to grasp the critical power security features and analysis the weak spots in power system. The numerical tests on power system show that the proposed algorithm can effectively select out the crucial status features with high accuracy.
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References
Tang, Y., Wang, Y.T., Tian, F., et al.: Research and development of stability analysis, early-warning and control system for huge power grid. Power Syst. Technol. 36(7), 1–11 (2012)
Cheng, X.Q., Jin, X.L., Wang, Y.Z., et al.: Survey on big data system and analytic technology. J. Softw. 25(9), 1889–1908 (2014)
Zhang, D.X., Miao, X., Liu, L.P., et al.: Research on development strategy for smart grid big data. Proc. CSEE 35(1), 2–12 (2015)
Poudel, S., Ni, Z., Malla, N.: Real-time cyber physical system testbed for power system security and control. Int. J. Electr. Power Energy Syst. 90, 124–133 (2017)
Zhang, B.M.: Concept extension and prospects for modern energy control centers. Autom. Electr. Power Syst. 27(15), 1–6 (2003)
Sun, H.B., Xie, K., Jiang, W.Y., et al.: Automatic operator for power systems: principle and prototype. Autom. Electr. Power Syst. 31(16), 1–6 (2007)
Huang, T.E., Sun, H.B., Guo, Q.L., et al.: Knowledge management and security early warning based on big simulation data in power grid operation. Power Syst. Technol. 39(11), 3080–3087 (2015)
Sun, H.B., Huang, T.E., Guo, Q.L., et al.: Power grid intelligent security early warning technology based on big simulation data. South. Power Syst. Technol. 10(3), 42–46 (2016)
Huang, T.E., Sun, H.B., Guo, Q.L., et al.: Distributed security feature selection online based on big data in power system operation. Autom. Electr. Power Syst. 40(4), 32–40 (2016)
Zhao, F., Sun, H.B., Zhang, B.M.: Zone division based automatic discovery of flowgate. Autom. Electr. Power Syst. 35(5), 42–46 (2011)
Jiang, W.Y., Sun, H.B., Zhang, B.M., et al.: Fine operational rule of power system. Proc. CSEE 29(4), 1–7 (2009)
Sun, H.B., Zhao, F., Jiang, W.H., et al.: Framework and functions of fine operational rules online automatic discovery system for power grid. Autom. Electr. Power Syst. 35(18), 81–86 (2011)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)
Kullback, S.: Information Theory and Statistics. Courier Corporation, Chelmsford (1997)
Jiang, W.Y., Zhang, B.M., Wu, W.C., et al.: A total transfer capability calculation method for power system operation and decision. Autom. Electr. Power Syst. 32(10), 12–17 (2008)
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Yang, J., Liu, S., Tao, W., Hu, C. (2018). A Distributed Security Feature Selection Algorithm Based on K-means in Power Grid System. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_40
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DOI: https://doi.org/10.1007/978-3-030-00009-7_40
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