References

FEATURE SELECTION BY KERNELIZED FUZZY ROUGH SETS FOR TRANSIENT STABILITY ASSESSMENT BASED ON GAUSSIAN PROCESS


[1] P. M. Anderson and A. A. Fouad, Power System Control and Stability, 2nd Edition, Piscataway, NJ: IEEE, 2003.

[2] L. Wehenkel, M. Pavella, E. Euxibie and B. Heilbronn, Decision tree based transient stability method a case study, IEEE Trans. Power Systems 9 (1994), 459-469.

[3] L. S. Moulin, A. P. A. da Silva, M. A. El-Sharkawi and R. J. Marks II, Support vector machines for transient stability analysis of large-scale power systems, IEEE Trans. Power Systems 19 (2004), 818-825.

[4] Sun Kai, S. Likhate, V. Vittal, V. S. Kolluri and S. Mandal, An online dynamic security assessment scheme using phasor measurements and decision trees, IEEE Transactions on Power Systems 22 (2007), 1935-1943.

[5] N. Amjady and S. F. Majedi, Transient stability prediction by a hybrid intelligent system, IEEE Trans. Power Systems 22 (2007), 1275-1283.

[6] F. R. Gomez, A. D. Rajapakse, U. D. Annakkage and I. T. Fernando, Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements, IEEE Trans. Power Systems 26 (2011), 1474-1483.

[7] Y. Xu, Z. Y. Dong, J. H. Zhao, P. Zhang and K. P. Wong, A reliable intelligent system for real-time dynamic security assessment of power systems, IEEE Trans. Power Systems 27 (2012), 1253-1263.

[8] C. A. Jensen, M. A. El-Sharkawi and R. J. Marks, Power system security assessment using neural networks: Feature selection using Fisher discrimination, IEEE Trans. Power Systems 16 (2001), 757-763.

[9] S. K. Tso and X. P. Gu, Feature selection by separability assessment of input spaces for transient stability classification based on neural networks, International Journal of Electrical Power & Energy Systems 26 (2004), 153-162.

[10] Harinder Sawhney and B. Jeyasurya, A feed-forward artificial neural network with enhanced feature selection for power system transient stability assessment, Electric Power Systems Research 76(12) (2006), 1047-1054.

[11] R. W. Swiniarski and A. Skowron, Rough set methods in feature selection and recognition, Pattern Recognition Letters 24(6) (2003), 833-849.

[12] R. Jensen and Q. Shen, Fuzzy-rough sets assisted attribute selection, IEEE Trans. Fuzzy Systems 15(1) (2007), 73-89.

[13] Qinghua Hu, Daren Yu, Witold Pedrycz and Degang Chen, Kernelized fuzzy rough sets and their applications, IEEE Trans. Knowledge and Data Engineering 23 (2011), 1649-1667.

[14] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, Cambridge, 2006.

[15] H. Nickisch and C. E. Rasmussen, Approximations for binary Gaussian process classification, Journal of Machine Learning Research 9 (2008), 2035-2078.

[16] Fei Cheng, Jiangsheng Yu and Huilin Xiong, Facial expression recognition in JAFFE dataset based on Gaussian process classification, IEEE Trans. Neural Networks 21 (2010), 1685-1690.

[17] Mahdi Jadaliha, Yunfei Xu, Jongeun Choi, N. S. Johnson and Weiming Li, Gaussian process regression for sensor networks under localization uncertainty, IEEE Trans. Signal Processing 61(2) (2013), 223-237.

[18] A. G. Phadke and J. S. Thorp, Synchronized Phasor Measurements and their Applications, Springer, New York, 2008.

[19] C. W. Taylor, D. C. Erickson, K. Martin, R. E. Wilson and V. Venkatasubramanian, WACS-wide-area stability and voltage control system: R&D and online demonstration, Proceedings of the IEEE 93 (2005), 892-906.

[20] V. Terzija, G. Valverds, Cai Deyu, P. Regulski, V. Madani, J. Fitch, S. Skok, M. M. Begovic and A. Phadke, Wide-area monitoring, protection, and control of future electric power networks, Proceedings of the IEEE 99 (2011), 80-93.

[21] B. Moser, On representing and generating kernels by fuzzy equivalence relations, Journal of Machine Learning Research 7 (2006), 2603-2620.

[22] L. Yu and H. Liu, Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research 5 (2004), 1205-1224.