Page 508 - Decision Making Applications in Modern Power Systems
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468  Decision Making Applications in Modern Power Systems


            11. Z. Moravej, M. Pazoki, M. Khederzadeh, New pattern-recognition method for fault analysis
                in transmission line with UPFC, IEEE Trans. Power Delivery 30 (3) (2015) 1231 1242.
            12. P.K. Mishra, A. Yadav, M. Pazoki, A Novel Fault Classification Scheme for Series
                Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier,
                IEEE Access, 2018.
            13. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, et al., The empirical
                mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series
                analysis, Proc. R. Soc. Lond. Ser. A: Math. Phys. Eng. Sci. 454 (1971) (1998) 903 995.
            14. Z.H.U. Zhihui, Y. Sun, Fault classification for power transmission line using EMD-
                approximate entropy and SVM, Electr. Power Autom. Equip. 7 (2008) 023.
            15. M.G. Frei, I. Osorio, Intrinsic time-scale decomposition: time frequency energy analysis
                and real-time filtering of non-stationary signals, Proc. R. Soc. Lond. Ser. A: Math. Phys.
                Eng. Sci. 463 (2078) (2007) 321 342.
            16. M. Pazoki, A new fault classifier in transmission lines using intrinsic time decomposition,
                IEEE Trans. Ind. Inf. 14 (2) (2018) 619 628.
            17. B. Ghoraani, Selected topics on time-frequency matrix decomposition analysis, J. Pattern
                Recognit. Intell. Syst. 1 (3) (2013) 64 78.
            18. G. Chandrashekar, F. Sahin, A survey on feature selection methods, Comput. Electr. Eng.
                40 (1) (2014) 16 28.
            19. T.N. Lal, O. Chapelle, J. Weston, A. Elisseeff, Embedded methods, Feature Extraction,
                Springer, Berlin, Heidelberg, 2006, pp. 137 165.
            20. H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of max-
                dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell.
                27 (8) (2005) 1226 1238.
            21. B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 2007.
            22. M. Kezunovic, Smart fault location for smart grids, IEEE Trans. Smart Grid 2 (1) (2011)
                11 22.
            23. K. Chen, C. Huang, J. He, Fault detection, classification and location for transmission lines
                and distribution systems: a review on the methods, High Voltage 1 (1) (2016) 25 33.
            24. V.H. Ferreira, R. Zanghi, M.Z. Fortes, G.G. Sotelo, R.B.M. Silva, J.C.S. Souza, et al., A
                survey on intelligent system application to fault diagnosis in electric power system trans-
                mission lines, Electr. Power Syst. Res. 136 (2016) 135 153.
            25. A. Ghaderi, H.L. Ginn III, H.A. Mohammadpour, High impedance fault detection: a
                review, Electr. Power Syst. Res. 143 (2017) 376 388.
            26. D.P. Mishra, P. Ray, Fault detection, location and classification of a transmission line,
                Neural Comput. Appl. 30 (2018) 1377 1424.
            27. A. Yadav, Y. Dash, An overview of transmission line protection by artificial neural net-
                work: fault detection, fault classification, fault location, and fault direction discrimination,
                Adv. Artif. Neural Syst. 2014 (2014) 12.
            28. S.A. Khaparde, N. Warke, S.H. Agarwal, An adaptive approach in distance protection
                using an artificial neural network, Electr. Power Syst. Res. 37 (1) (1996) 39 44.
            29. A.J. Mazon, I. Zamora, J.F. Minambres, M.A. Zorrozua, J.J. Barandiaran, K. Sagastabeitia,
                A new approach to fault location in two-terminal transmission lines using artificial neural
                networks, Electr. Power Syst. Res. 56 (3) (2000) 261 266.
            30. A. Jain, V.S. Kale, A.S. Thoke, Application of artificial neural networks to transmission
                line faulty phase selection and fault distance location, in: Proceedings of the IASTED
                International Conference “Energy and Power System”, Chiang Mai, Thailand, 2006,
                pp. 262 267.
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