Power System Protection Using Machine Learning Technique

[1]  Gavin C. Cawley,et al.  Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.

[2]  O.A.S. Youssef,et al.  Combined fuzzy-logic wavelet-based fault classification technique for power system relaying , 2004, IEEE Transactions on Power Delivery.

[3]  O.P. Malik,et al.  Transmission line distance protection based on wavelet transform , 2004, IEEE Transactions on Power Delivery.

[4]  José A. Aguado,et al.  Wavelet-based ANN approach for transmission line protection , 2003 .

[5]  森 雅夫,et al.  Extracting Feature Subspace for Kernel Based Support Vector Machines , 2002 .

[6]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[7]  Samy Bengio,et al.  SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..

[8]  Ganapati Panda,et al.  A novel fuzzy neural network based distance relaying scheme , 2000 .

[9]  Thorsten Joachims,et al.  Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Ganapati Panda,et al.  Adaptive relay setting for flexible AC transmission systems (FACTS) , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[12]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[13]  David R. Musicant,et al.  Successive overrelaxation for support vector machines , 1999, IEEE Trans. Neural Networks.

[14]  A. A. Tasnimi.  Seismic Behavior of Prefabricated Column-Footing Connection , 1999 .

[15]  Christopher J. C. Burges,et al.  Geometry and invariance in kernel based methods , 1999 .

[16]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[17]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[18]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[19]  Mehrdad Ghandhari,et al.  Improving power system dynamics by series-connected FACTS devices , 1997 .

[20]  Dana Ron,et al.  Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation , 1997, Neural Computation.

[21]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  A. T. Johns,et al.  PROTECTION SCHEME FOR EHV TRANSMISSION SYSTEMS WITH THYRISTOR CONTROLLED SERIES COMPENSATION USING RADIAL BASIS FUNCTION NEURAL NETWORKS , 1997 .

[23]  A. T. Johns,et al.  Artificial neural-network-based protection scheme for controllable series-compensated EHV transmission lines , 1996 .

[24]  B. Kulicke,et al.  Neural network approach to fault classification for high speed protective relaying , 1995 .

[25]  Einar Vaughn Larsen,et al.  Characteristics and rating considerations of thyristor controlled series compensation , 1994 .

[26]  G. G. Karady,et al.  Investigations of an advanced form of series compensation , 1994 .

[27]  Subhransu Ranjan Samantaray,et al.  An accurate fault classification algorithm using a minimal radial basis function neural network , 2004 .

[28]  Avinash Kumar Sinha,et al.  A wavelet multiresolution analysis for location of faults on transmission lines , 2003 .

[29]  Jesper Salomon,et al.  Support Vector Machines for Phoneme Classification , 2001 .

[30]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.