Multi-view synergistic enhanced fault recording data for transmission line fault classification

Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning-based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation-based augmentation) may lead to distortion of multi-view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi-view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi-view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real-world datasets validate the effectiveness of the proposed method. This study proposes a transmission line fault classification method via the multi-view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi-view data such as time and frequency domains of fault recording data and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. image