Ageing Condition Assessment of Transformer Insulation in Visual Domain Using DCNN

Aniket Vatsa, Ananda Shankar Hati, V. Khadkikar
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引用次数: 0

Abstract

Identifying ageing characteristics is essential for mitigating the transformer's catastrophic failure. However, the traditional methods for assessing the effects of ageing, such as the degree of polarisation measurement of kraft paper, are destructive. Transformers' ageing characteristics can be extracted from dielectric response analysis using frequency domain spectroscopy's low-frequency band. However, measuring frequency domain spectroscopy (FDS) in low-frequency regions is time-consuming. This research developed a unique deep convolutional neural network (DCNN) based ageing state identification approach by converting FDS into a visual domain using Markov Transition Field (MTF) to ascertain the hidden ageing attributes. Thus enhancing the ageing features and automatic feature extraction is utilised for ageing state diagnosis. The proposed MTF-DCNN method has a 96.60% diagnosis accuracy across five ageing categories. It also provides an automatic feature extraction-based network for a detailed evaluation of the transformer's insulation health.
基于DCNN的变压器绝缘老化状态视觉评估
确定老化特性对于减轻变压器的灾难性故障至关重要。然而,评估老化影响的传统方法,如测量牛皮纸的极化程度,是具有破坏性的。利用频域光谱的低频波段分析变压器的介质响应,可以提取变压器的老化特性。然而,在低频区域测量频域谱(FDS)非常耗时。本研究提出了一种独特的基于深度卷积神经网络(DCNN)的老化状态识别方法,利用马尔科夫过渡场(MTF)将FDS转换为视觉域来确定隐藏的老化属性。因此,利用增强老化特征和自动特征提取来进行老化状态诊断。所提出的MTF-DCNN方法在5个老化类别中的诊断准确率为96.60%。它还提供了一个基于自动特征提取的网络,用于详细评估变压器的绝缘健康状况。
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