Radial Deformation Emplacement in Power Transformers Using Long Short-Term Memory Networks
This work provides an incremental improvement in diagnosing and locating radial deformation faults in power transformers for maintenance engineers, potentially preventing costly failures.
This paper addresses the problem of radial deformation (RD) in power transformers, which can lead to short circuit faults and insulation damages. It proposes using Long Short-Term Memory (LSTM) networks as a feature extraction technique to diagnose and locate these RD faults in their early stages, demonstrating its effectiveness experimentally.
A power transformer winding is usually subject to mechanical stress and tension because of improper transportation or operation. Radial deformation (RD) is an example of mechanical stress that can impact power transformer operation through short circuit faults and insulation damages. Frequency response analysis (FRA) is a well-known method to diagnose mechanical defects in transformers. Despite the precision of FRA, the interpretation of the calculated frequency response curves is not straightforward and requires complex calculations. In this paper, a deep learning algorithm called long short-term memory (LSTM) is used as a feature extraction technique to locate RD faults in their early stages. The experimental results verify the effectiveness of the proposed method in the diagnosis and locating of RD defects.