Real-time security monitoring of electric power systems using parallel associative memories
- 4 December 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2929-2932
- https://doi.org/10.1109/iscas.1990.112624
Abstract
A methodology for the monitoring of power system security, developed with emphasis on the efficient processing of large amounts of data gathered through communication channels in real time, is presented. The approach is based on the adaptive pattern recognition concept and its implementation on parallel distributed computational architectures of artificial neural networks. Clusterwise continuous associative maps are established through data self-organization encompassing a variety of system operating regimes and topological configurations of the transmission network. Accurate and very fast information retrieval characterizes the real-time behavior of the distributed information processing system Author(s) Sobajic, D.J. Case Western Res. Univ., Cleveland, OH, USA Pao, Y.-H. ; Njo, W. ; Dolce, J.L.Keywords
This publication has 7 references indexed in Scilit:
- On-line monitoring and diagnosis of powersystem operating conditions using artificial neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Robust control of nonlinear systems using pattern recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Analysis of transients on basis of identification of signal generative structure: Even unto chaosPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- A perspective on research aimed at understanding the systems nature of neural controllersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Operation of the Large Interconnected Power System by Decision and ControlIEEE Transactions on Power Apparatus and Systems, 1980
- Operating under stress and strain [electrical power systems control under emergency conditions]IEEE Spectrum, 1978
- Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold ElementsIEEE Transactions on Computers, 1972