Assessment of Drying Quality for Power Transformer during Manufacturing Process Using Variation of Transfer Function

The drying process is one of the most important tasks in transformers manufacturing stages. The drying quality in this stage is directly proportional to transformer lifetime. In this contribution, drying process at manufacturing site has been evaluated by use of frequency response analysis (FRA). The measured transfer functions of power transformer during the stage of drying are analyzed. Using artificial neural network (ANN), a method has been proposed to give an estimate for required time for drying process. Results show that the ANN could well forecast the required time for drying if the ANN is trained using the measured patterns. The estimation obtained from this method is valid for all the transformers which have the same design.