ALGORITHM FOR TRAINING THE ARTIFICIAL NEURAL NETWORK OF FACTOR PREDICTING THE POWER CABLE LINES INSULATING MATERIALS LIFE

Abstract

The article is devoted to the research of thermofluxtual processes in accordance with the theory of thermal conductivity for solving the problems of factor prediction of the residual life of insulating materials based on the non-destructive temperature method. The relevance of the task of developing algoritma for predicting the temperature of SCL cores in real time based on the data of the temperature monitoring system, taking into account the change in the current load of the line and external heat removal conditions, is justified. The experimental method revealed the types of artificial neural networks, their architecture and composition, which provide maximum prediction accuracy with a minimum set of significant factors. A neural network has been developed to determine the temperature regime of the current-carrying core of the power kawhite. The minimum set of significant factors and the dimension of the input training vector is determined, which provides the versatility of the neural network prediction method. A neural network for determining the temperature mode of the current-carrying core is designed to diagnose and predict the electrical insulation (EI) life of a power cable. The model allows assessing the current isolation state and predicting the residual resource of the SCL. Comparative analysis of experimental and calculated characteristics of learning algorithms of isostic neural is carried out. It has been found that the proposed algorithm of artificial neural network can be used for prediction of current-carrying core temperature mode, three hours in advance with accuracy up to 2.5% of actual value of core temperature. The main field of application of the developed neural network for determining the temperature mode of the current-carrying core is in di-agnostics and predicting the electrical insulation (EI) life of the power cable. The development of an intelligent system for predicting the temperature of the LCS core contributes to the planning of the operation modes of the electric network in order to increase the reliability and energy efficiency of their interaction with the integrated energy system.