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A power transformer winding is usually subject to mechanical stress andtension because of improper transportation or operation. Radial deformation(RD) is an example of mechanical stress that can impact power transformeroperation through short circuit faults and insulation damages. Frequencyresponse analysis (FRA) is a well-known method to diagnose mechanical defectsin transformers. Despite the precision of FRA, the interpretation of thecalculated frequency response curves is not straightforward and requirescomplex calculations. In this paper, a deep learning algorithm called longshort-term memory (LSTM) is used as a feature extraction technique to locate RDfaults in their early stages. The experimental results verify the effectivenessof the proposed method in the diagnosis and locating of RD defects.

Radial Deformation Emplacement in Power Transformers Using Long Short-Term Memory Networks