• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2019-09 (Vol.68 No.09)
  4. 10.5370/KIEE.2019.68.9.1060
XML PDF INFO REF

References

1 
A. R. Bergen, V. Vittal, 2000, Power System Analysis, 2nd ed., Prentice HallGoogle Search
2 
H. Saadat, 2002, Power System Analysis, 2nd ed., McGraw-HillGoogle Search
3 
G. W. Kim, S. H. Hyun, Feb 2005, Power System Analysis Using MATLAB 1, UUPGoogle Search
4 
J. D. Glover, T. J. Overbye, M.S. Sarma, 2016, Power System Analysis & Design, 6th ed., Cengage LearningGoogle Search
5 
S. R. Nam, J. K. Hong, S. H. Kang, J. K. Park, 2004, Analysis of characteristic frequency along fault distance on a transmission line, KIEE Trans., Vol. 53a, No. 8, pp. 432-437Google Search
6 
D. G. Lee, S. H. Kang, 2010, Distance relaying algorithm using a DFT-based modified phasor estimation method, KIEE Trans., Vol. 59, No. 8, pp. 1360-1365DOI
7 
A. P. Sakis Meliopoulos, G. J. Cokkinides, P. Myrda, Y. Liu, F. Rui, L. Sun, R. Huang, Z. Tan, 2017, Dynamic state estimation-based protection: Status and Promise, IEEE Trans. Power Delivery, Vol. 32, No. 1, pp. 320-330DOI
8 
S. Paudyal, G. Ramakrishna, M. S. Sachdev, 2010, Application of equal area criterion conditions in the time domain for out-of-step protection, IEEE Trans. Power Delivery, Vol. 25, No. 2, pp. 600-609DOI
9 
E. Farantatos, R. Huang, G. J. Cokkinides, Aug 2016, A predictive generator out-of-step protection and transient stability monitoring scheme enabled by a distributed dynamic state estimator, IEEE Trans. Power Del., Vol. 31, No. 4, pp. 1826-1835DOI
10 
Y. Cui, R. G. Kavasseri, S. M. Brahma, 2017, Dynamic state estimation assisted out-of-step detection for generators using angular difference, IEEE Trans. Power Delivery, Vol. 32, No. 3, pp. 1441-1449DOI
11 
A. Levant, 1998, Robust exact differentiation via sliding mode technique, Automatica, Vol. 34, No. 3, pp. 379-384DOI
12 
Y. I. Son, I. H. Kim, May 2010, A robust state observer using multiple integrators for multivariable LTI systems, IEICE Trans. Fundamentals, Vol. E93-A, No. 5, pp. 981-984DOI
13 
W. H. Chen, J. Yang, L. Guo, S. Li, 2016, Disturbance observer-based control and related methods-An overview, IEEE Trans. Ind. Electron., Vol. 63, No. 2, pp. 1083-1095DOI
14 
H. Shim, G. Park, Y. Joo, J. Back, N. H. Jo, 2016, Yet another tutorial of disturbance observer: robust stabilization and recovery of nominal performance, Control Theory and Technology, Vol. 14, No. 3, pp. 237-249DOI
15 
J. Chang, G. N. Taranto, J. H. Chow, 1997, Dynamic state estimation using a nonlinear observer for optimal series- capacitor switching control, Int. J. Electrical Power & Energy Systems, Vol. 19, No. 7, pp. 441-447DOI
16 
R. S. Sutton, Aug 1991, Dyna, an integrated architecture for learning, planning, and reacting, ACM SIGART Bulletin, Vol. 2, No. 4, pp. 160-163DOI
17 
C. J. C. H. Watkins, P. Dayan, May 1992, Q-learning, Machine Learning, Vol. 8, No. 3-4, pp. 279-292DOI
18 
R. S. Sutton, A. G. Barto, 1998, Reinforcement Learning: An Introduction, MIT pressGoogle Search
19 
S. Russel, P. Norvig, Jan 2003, Artificial Intelligence: A Modern Approach, Prentice HallGoogle Search
20 
A. Karami, 2008, Radial basis function neural network for power system transient energy margin estimation, Journal of Electrical Engineering & Technology, Vol. 3, No. 4, pp. 468-475DOI
21 
M. J. Reddy, D. K. Mohanta, 2008, Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of powr swings, IET Generation, Transmission & Distribution, Vol. 2, No. 2, pp. 235-244DOI
22 
X. Glorot, Y. Bengio, 2010, Understanding the difficulty of training deep feedforward neural networks, in Proc. of the 13th Int. Conf. Artificial Intelligence and Statistics, Vol. 9, pp. 249-256Google Search
23 
W. Yao, J. Fang, P. zhao, S. Liu, J. Wen, S. Wang, 2013, TCSC nonlinear adaptive damping controller design based on RBF nerual network to enhance power system stability, Journal of Electrical Engineering & Technology, Vol. 8, No. 2, pp. 252-261DOI
24 
D. P. Kingma, J. Ba, 2015, Adam: a method for stochastic optimization, ICLRGoogle Search
25 
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, Marc G. Bellemare, A. Graves, M. Riedmiller, Andreas K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis, Feb 2015, Human-level control though deep reinforcement learning, Nature, Vol. 518, No. , pp. 529-533DOI
26 
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. V. D. Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Jan 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529, pp. 484-489DOI
27 
S. Das, R. Dubey, B. K. Panigrahi, S. R. Samantaray, 2017, Secured zone-3 protection during power swing and voltage instability: an online approach, IET Generation, Trans- mission & Distribution, Vol. 11, No. 2, pp. 437-446DOI
28 
A. Juliani, 2017, Simple Reinforcement Learning with Tensorflow, Hanbit Publishing NetworkGoogle Search