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

Abstract

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.

Evaluation of numerical indices for the assessment of transformer frequency response

“…In the last case, the FRA traces of a 50 MVA, 132/33 kV transformer are measured during the drying process. When the transformer dries, the volume of the paper decreases due to water loss and, therefore, the winding experiences a change in its height [32]. Consequently, the traces measured in the drying process acts similar to an axial displacement.…”

Transfer Function Analysis to Evaluate Drying Quality of Power Transformers by Support Vector Machine

“…This method is based on the fact that a steep impulse or step voltage is applied to the winding as input and simultaneously the voltage or current at the other terminal be measured as output. If the input signal involves enough frequency components to excite all desired oscillatory modes of the winding, the frequency behavior or TF can be extracted accurately [28].…”

“…In this paper, TF measurements have been performed for the active part of a 50MVA 132KV/33KV power transformer while the active part had been placed in the drying chamber in dierent time intervals during drying process [10]. The measured TFs for three dierent time intervals (2, 4, and 8 hours) during early hours of process Therefore, indirectly TFs are compared through examination of these coecients which can be detected easily by VF method [24].…”

“…In [10] a method based on application of FRA technique and articial neural network (ANN) is introduced for comparison of TFs to evaluate drying quality of transformers. Although the mentioned study gave important results, such results are not ecient to evaluate drying quality of power transformers without performing additional investigations.…”

A new method for evaluation of transformer drying process using transfer function analysis and artificial neural network

“…In order to better understanding of author's idea, the drying process is described in the following. Drying process is one of the most important stages in transformers manufacturing stages which its quality is directly proportional with transformer lifetime [11]. If drying process is done well, the failure of this equipments will also greatly reduced.…”