NEUROCOMPUTER CONTROL OF CABLE NETWORKS BANDWIDTH THROUGH ACCOUNTING AND CONTROL OF THEIR PARAMETERS
Abstract
The article discusses a neurocomputer system for predicting the resource of a power cable
line (РCL) using neural network technologies. A hardware modular implementation of a
neurocomputer (NC) implemented on the basis of FPGA was selected. To solve the problem of
predicting thermal processes of РCL, it was decided to use a NeuroMatrix NM6404 digital
neurochip with a variable structure due to their high performance compared to power consumption,
a high degree of versatility. To predict the temperature conditions of the РCL, an artificial
neural network (INS) was developed to determine the current temperature regime for the currentcarrying
core of the РCL. The architecture of the INS for the implementation of the NC of the SCL
temperature prediction system has been selected, which allows for long-term prediction of РCL
temperatures in real time. The choice of the activation function of the INS neurons for the implementation
of the NC of the SCL temperature prediction system, which allows for a long-term forecast
of SCL temperatures without increasing the error with an increase in the forecast range. The
proposed neural network algorithm that predicts the characteristics of the electrical insulation of
the РCL, based on the sliding window method for predicting time series, was tested on a control
sample of experimental data not included in the sample for training the INS. Experimental studies
of the proposed adaptive forecasting method have been carried out, namely, an adaptive algorithm
has been developed and the prediction of thermal processes in the isolation of the SCL from the
load current has been performed. Analysis of the results showed that the longer the aging time, the
greater the temperature difference between the original and aged sample. When analyzing the
data obtained, it was determined that the maximum deviation of the data obtained from the INS
during the experiment from the data in the training sample was less than 3%, which is quite acceptable
for this study result. It is shown that the developed methods and algorithms are elements
of an integrated power grid management system, and the developed adaptive NC model makes it
possible to assess the current state of insulation and predict the remaining life of the РCL.
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