Short-term prediction of oil temperature change of an indoor transformer by self-organizing map (SOM)

This paper considers an application of the Self-Organizing Map (SOM), an effective technique for clustering of multi-dimensional data, to the short-term prediction of the oil temperature change of an indoor transformer. Due to the heavy load during the summer, the SOM is obtained from the learning with oil temperature and atmospheric temperature in the summer season. The prediction of the oil temperature of the transformer can be realized by the SOM based on the maximum and minimum values of the forecast atmospheric temperature announced by the meteorological observatory. Using this technique, the change of the oil temperature of the transformer is well predicted, and the prediction accuracy is higher than that obtained using the conventional method.