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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.