Prediction Model for Trends in Submarine Cable Burial Depth Variation Considering Dynamic Thermal Resistance Characteristics

Zhenxing Hu, Xueyong Ye, Xiaokang Luo, Hao Zhang, Mingguang He, Jiaxing Li and Qian Li ()
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Zhenxing Hu: Southwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Chengdu 610021, China
Xueyong Ye: Southwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Chengdu 610021, China
Xiaokang Luo: Southwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Chengdu 610021, China
Hao Zhang: Southwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Chengdu 610021, China
Mingguang He: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
Jiaxing Li: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
Qian Li: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China

Energies, 2024, vol. 17, issue 9, 1-15

Abstract: Fault problems associated with submarine cables caused by variations in their burial depth are becoming increasingly prominent. To address the difficulty of detecting the burial depth of submarine cables and trends in its variation, a prediction model for submarine cable burial depth was proposed which considers the dynamic characteristics of thermal resistance. First, a parallel thermal circuit model of a three-core submarine cable was established, and a formula for calculating the submarine cable’s burial depth was derived based on a formula for calculating the submarine cable’s core temperature. Then, the calculation result was corrected by considering the dynamic characteristics of the thermal resistance of the submarine cable’s structural materials. On this basis, feature vectors associated with the seabed cable burial depth calculation data and time nodes were mined by a convolutional neural network and used as the input parameters of a long short-term memory network for optimization and training, and a prediction model for trends in seabed cable burial depth variation was obtained. Finally, an example analysis was carried out based on the actual electrical parameter data of submarine cables buried by an offshore oil and gas platform. The results showed that the prediction model for trends in variations in the burial depth of submarine cables based on the CNN-LSTM neural network can achieve high prediction accuracy and prediction efficiency.

Keywords: three-core submarine cable; parallel thermal circuit model; dynamic characteristics of thermal resistance; CNN-LSTM neural network; prediction model of burial trend (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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