Digital Signal Processing in Power System Protection and Control

Digital Signal Processing in Power System Protection and Control bridges the gap between the theory of protection and control and the practical applications of protection equipment. Understanding how protection functions is crucial not only for equipment developers and manufacturers, but also for their users who need to install, set and operate the protection devices in an appropriate manner.After introductory chapters related to protection technology and functions, Digital Signal Processing in Power System Protection and Control presents the digital algorithms for signal filtering, followed by measurement algorithms of the most commonly-used protection criteria values and decision-making methods in protective relays. A large part of the book is devoted to the basic theory and applications of artificial intelligence techniques for protection and control. Fuzzy logic based schemes, artificial neural networks, expert systems and genetic algorithms with their advantages and drawbacks are discussed. AI techniques are compared and it is also shown how they can be combined to eliminate the disadvantages and magnify the useful features of particular techniques.The information provided in Digital Signal Processing in Power System Protection and Control can be useful for protection engineers working in utilities at various levels of the electricity network, as well as for students of electrical engineering, especially electrical power engineering. It may also be helpful for other readers who want to get acquainted with and to apply the filtering, measuring and decision-making algorithms for purposes other than protection and control, everywhere fast and on-line signal analysis is needed for proper functioning of the apparatus.

[1]  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).

[2]  W. Rebizant,et al.  Current transformer saturation detection with genetically optimized neural networks , 2005, 2005 IEEE Russia Power Tech.

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

[4]  S. Vemuri,et al.  Neural network based short term load forecasting , 1993 .

[5]  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).

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

[7]  K Feser.  ANN BASED DETECTION OF OS CONDITIONS IN POWER SYSTEM , 2001 .

[8]  L. Schiel,et al.  Correction of Current Transformer Transient Performance , 2008, IEEE Transactions on Power Delivery.

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

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

[11]  Waldemar Rebizant,et al.  CT Saturation Correction Based on the Estimated CT Saturation Time Constant , 2008 .

[12]  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.

[13]  A. Wiszniewski,et al.  A multi-criteria differential transformer relay based on fuzzy logic , 1995 .

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

[15]  L. Schiel,et al.  New Fuzzy Stabilisation Scheme Against Transformer in Rush Currents , 2008 .

[16]  B. Kasztenny,et al.  CT Saturation In Industrial Applications - Analysis and Application Guidelines , 2008 .

[17]  R. E. Uhrig.  Potential application of neural networks to the operation of nuclear power plants , 1991 .

[18]  Waldemar Rebizant,et al.  Prediction of CT Saturation Period for Differential Relay Adaptation Purposes , 2004 .

[19]  D. E. Rumelhart,et al.  chapter Parallel Distributed Processing, Exploration in the Microstructure of Cognition , 1986 .

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

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

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

[23]  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.