ANALYSIS AND SELECTION OF METHODOLOGIES IN THE SOLUTION OF THE PROBLEMS OF INTELLECTUALIZATION IN SYSTEMS FOR PROGNOSIS OF THERMOFLUCTUATION PROCESSES IN CABLE NETWORKS
Abstract
The article is devoted to research on the creation of diagnostics and prediction of
thermofluctuation processes of insulating materials of power cable lines (PCL) of electric power
systems based on such methods of artificial intelligence as neural networks and fuzzy logic. The
necessity of developing a better methodology for the analysis of thermal conditions in PCL is
shown. The urgency of the task of creating neural networks (NS) for assessing the throughput,
calculating and predicting the temperature of PCL conductors in real time based on the data of
the temperature monitoring system, taking into account changes in the current load of the line and
the external conditions of the heat sink, is substantiated. Based on the main criteria, traditional
and neural network algorithms for forecasting are compared, and the advantage of NS methods is
shown. The classification of NS methods and models for predicting the temperature conditions of
cosmic rays has been carried out. To solve the problem of forecasting the PCL resource, a network
was selected with direct data distribution and back propagation of the error, because networks of this type, together with an activation function in the form of a hyperbolic tangent, are to
some extent a universal structure for many problems of approximation, approximation, and forecasting.
A neural network has been developed to determine the temperature regime of a currentcarrying
core of a power cable. A comparative analysis of the experimental and calculated characteristics
of the temperature distributions was carried out, while various load modes and the
functions of changing the cable current were investigated. When analyzing the data, it was determined
that the maximum deviation of the data received from the neural network from the data of
the training sample was less than 2.5 %, which is an acceptable result. To increase the accuracy, a
large amount of input and output data was used when training the network, as well as some refinement
of its structure. The model allows you to evaluate the current state of isolation and predict
the residual life of PCL. The model can be used in devices and systems for continuous diagnosis
of power cables by temperature conditions.
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