A neural network model for predicting the temperature of insulated overhead lines

In this work, we develop a neural network model based on experimental heating and cooling curves of insulated wires of overhead lines under changes in wind speed and its direction relative to the wire axis. Insulated SIP-3 wire used in overhead lines was investigated. A multilayer perceptron neural network was employed to model the process of heating and cooling of insulated wire under changes in wind speed and its direction. The average absolute error was taken as a criterion for evaluating the prediction accuracy of wire core and insulation temperatures. The main parameters of the developed neural network model include the number of hidden layers and neurons in each hidden layer, the degree of regulation, and the regulatory rigidity of the model. The modelled heating and cooling curves of insulated wire were compared with those obtained experimentally. The average absolute error was equal to 1.74 and –4.08°C for the predicted core and insulation temperatures, respectively. The difference between the heating curves at low wind speeds was found to range within 9°C. It was shown that an increase in wind speed leads to a decrease in the difference between the curves. Our analysis showed that neural network models used for predicting variations in the temperature of insulated overhead lines should be trained using a larger number of input parameters. This is the main prerequisite for high prediction accuracy of such models, when the difference between the simulated and experimental data does not exceed 5%.