VMD and self-attention mechanism-based Bi-LSTM model for fault detection of optical fiber composite submarine cables

Abstract

Abstract As the main electrical equipment of offshore power grids, optical fiber composite submarine cables undertake the task of power transmission and data communication. In order to ensure the proper functioning of the submarine cable, it is necessary to analyze the working state of it and identify the fault event. This paper proposes a fault detection method for submarine cables, that is, the VMD and self-attention-based Bi-LSTM model. First, we use ANSYS software to generate the vibration waveforms of three main fault events of optical fiber composite submarine cables. Then, by generating the detection matrix of background noise and the vibration waveforms, it can realize the orientation and detection of fault events in single submarine cable. In addition, the vibration signal can be decomposed into IMF components using variational mode decomposition (VMD) for feature extraction. Moreover, the IMF components are input to the self-attention layer for feature fusion and Bi-LSTM module for further feature extraction. Finally, the result of the fault detection is output through the classification layer. According to the comparative experiment and the ablation experiment, the proposed model has proved to outperform the other benchmark models and is robust and stable under the condition of different signal-to-noise ratios.

Keywords

• Fault detection of submarine cables
• Variational mode decomposition
• Bi-LSTM
• Deep learning