Short-Term Prediction of Oil Temperature Change of Outdoor Transformer by Self-Organizing ap (SOM)

Abstract

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 the transformer. For its application, three types of processes for the prediction are investigated, i.e., (1) A SOM is obtained by using atmospheric temperature, load rate and oil temperature in the past year, then the oil temperature except those for leaning the SOM is predicted. (2) Due to the heavy load during the summer, the SOM is obtained with every three months for the duration June through October. The oil temperature for the season is predicted using similar data structure as that of process (1). (3) The prediction of the oil temperature of transformers 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 an outdoor transformer is well predicted, and the prediction accuracy is higher than that obtained using the conventional method.