Application of Artificial Neural Networks

The signal processing methods and algorithms described in preceding chapters were expressed in form of explicit equations, transfer functions and/or logic rules, either in crisp or in fuzzy versions. There are, however, specific tasks and power system operation conditions when, especially for the problems that are complex and difficult to express in terms of traditional means, other solutions should be applied. In such situations, both for signal processing and decision making Artificial Neural Networks may constitute a good solution.

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

[2]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[3]  Q. Henry Wu,et al.  A neural network regulator for turbogenerators , 1992, IEEE Trans. Neural Networks.

[4]  Waldemar Rebizant,et al.  Evolutionary improvement of neural classifiers for generator out-of-step protection , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[5]  Mladen Kezunovic,et al.  NEURAL NETWORK APPLICATIONS TO REAL-TIME AND OFF-LINE FAULT ANALYSIS , 2001 .

[6]  O. T. Tan,et al.  Neural-net based real-time control of capacitors installed on distribution systems , 1990 .

[7]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[8]  K. P. Wong,et al.  Transient stability assessment for single-machine power systems using neural networks , 1990, IEEE TENCON'90: 1990 IEEE Region 10 Conference on Computer and Communication Systems. Conference Proceedings.

[9]  Hong-Jun Yoon,et al.  Correction of current transformer distorted secondary currents due to saturation using artificial neural networks , 2001 .

[10]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[11]  S. T. Glad,et al.  Estimation of the primary current in a saturated transformer , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[12]  Furong Li,et al.  Combined wavelet transform and regression technique for secondary current compensation of current transformers , 2002 .

[13]  R.K. Aggarwal,et al.  An Algorithm for Compensating Secondary Currents of Current Transformers , 1997, IEEE Power Engineering Review.

[14]  Sang-Hee Kang,et al.  New ANN-Based Algorithms for Detecting HIFs in Multigrounded MV Networks , 2008, IEEE Transactions on Power Delivery.

[15]  Eugeniusz Rosolowski,et al.  Current related relaying algorithms immune to saturation of current transformers , 1997 .

[16]  Hans-Heinrich Bothe,et al.  Neuro-Fuzzy-Methoden , 1998 .

[17]  Tommy W. S. Chow,et al.  Neural Networks and Computing - Learning Algorithms and Applications , 2007, Series in Electrical and Computer Engineering.

[18]  P. A. Crossley,et al.  Overcurrent protection using signals derived from saturated measurement CTs , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[19]  Françoise Fogelman Soulié,et al.  Neural networks and computing , 1991 .

[20]  A. T. Johns,et al.  Development and hardware implementation of a compensating algorithm for the secondary current of current transformers , 1996 .

[21]  L. Schiel,et al.  Transformer Differential Protection with Neural Network Based Inrush Stabilization , 2007, 2007 IEEE Lausanne Power Tech.

[22]  Jan Izykowski,et al.  Application of ANN methods for instrument transformer correction in transmission line protection , 2001 .

[23]  Ali Abur,et al.  Experimental evaluation of EMTP-based current transformer models for protective relay transient study , 1994 .

[24]  Robert Fullér,et al.  Introduction to neuro-fuzzy systems , 1999, Advances in soft computing.

[25]  M.A. El-Sharkawi,et al.  Neural network application to high performance electric drives systems , 1995, Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics.