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• MULTI-STAGE METHOD FOR SHORT-TERM FORECASTING OF TEMPERATURE CHANGES MODES IN THE POWER CABLE

  N.K. Poluyanovich, N.V. Azarov, A.V. Ogrenichev, M.N. Dubyago

  2020-07-20

  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. The proposed neural network algorithm for predicting the characteristics
  of electrical isolation was tested on a control sample of experimental data on which
  training of an artificial neural network was not carried out. The forecast results showed the effectiveness
  of the selected model. To solve the problem of PCL resource prediction, a network was
  selected with direct data distribution and back propagation of the error, because Networks of thistype, together with the 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 was developed to determine the temperature regime of a current-carrying 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 2.2 %, which is an acceptable result. The model can be used in devices and systems for
  continuous diagnosis of power cables by temperature conditions.

• NEUROCOMPUTER CONTROL OF CABLE NETWORKS BANDWIDTH THROUGH ACCOUNTING AND CONTROL OF THEIR PARAMETERS

  N.К. Poluyanovich, N. V. Azarov, М.N. Dubyago

  84-103

  2025-07-31

  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