Associative memory models for data processing

Abstract

This paper is an extension of work on a new framework for solving data acquisition and processing problems in power systems. The proposed methodology applies pattern analysis techniques to solve the network configuration, observability analysis and bad data processing problems. Other associative memory models are investigated for the solution of the observability analysis and bad data processing tasks. Special emphasis has been given to pattern analysis tools suitable for massively parallel implementations, such as artificial neural network models. Test results have been obtained for the IEEE 24- and 118-bus test systems.