Improvement of Information Protection and Data Transmission Methods in the Power Industry Using Neural Networks and a System of Residual Classes

Abstract

This article covers the issue of secure systems development in power industry. Such systems are developed by new systems of information protection, cyphering and coding. The research of the necessity to protect the microchip intelligent electronic devices has become the kea priority of energy system security. Coding is to be carried out through the implementation of neural networks which has significantly improved all the information protection systems of energy systems security. Implementation of cyphering neural networks models has solved a problem of overall coding and response time of the system. It was suggested to use systems of residual classes in order to raise stability index of cyphering algorithms as well as mathematical calculation such as for example neural network training. The transfer to non-positional notations such as systems of residual classes has raised the speed of mathematical calculation by 1.6 times.