Condition prediction of submarine cable based on CNN-BiGRU integrating attention mechanism

Abstract

As the lifeline of energy supply for various offshore projects, accurately evaluating and predicting the operation status of submarine cables are the foundation for the reliable operation of energy systems. Based on fully mining the dynamic and static characteristics of submarine cable operation and maintenance data, this paper proposes a submarine cable operation status prediction method based on a convolutional neural network—bidirectional gated recurrent unit (CNN-BiGRU) integrating attention mechanism. Firstly, the evaluation index system of the submarine cable operation status is established by considering three key influencing factors including online monitoring, routine inspection, and static test. Then, the operation condition evaluation model for submarine cable is constructed based on the cooperative game theory and the multi-level variable weight evaluation. Finally, the CNN-BiGRU combined neural network model integrating the attention mechanism is established, and the historical operation data and condition quantification results (health value) are used as input characteristic parameters to predict the evolution trend of the operation status of the submarine cable. The case study shows that the proposed method can effectively predict the operation status of submarine cables, and the root mean square error of the prediction is as low as 1.36%, which demonstrates the superior performance compared with the back propagation (BP) neural network, CNN, long short-term memory (LSTM), CNN-LSTM, and other algorithms.