论文标题
使用长的短期内存网络中的功率变压器中的径向变形物
Radial Deformation Emplacement in Power Transformers Using Long Short-Term Memory Networks
论文作者
论文摘要
由于运输或操作不当，电源变压器绕组通常会遭受机械应力和张力。径向变形（RD）是机械应力的一个示例，可以通过短路故障和绝缘损坏影响功率变压器的操作。频率响应分析（FRA）是诊断变压器机械缺陷的众所周知的方法。尽管FRA的精确度，但对计算的频率响应曲线的解释并不简单，需要复杂的计算。在本文中，一种称为长短期记忆（LSTM）的深度学习算法用作特征提取技术，以在早期阶段定位RD故障。实验结果验证了所提出的方法在RD缺陷的诊断和定位中的有效性。
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.
