Data Driven and Fuzzy Synthetic Evaluation Based Submarine Cable Health State Assessment

Abstract

Abstract—Health state assessment is of great importance to ensure the safe and stable operation of submarine cables. Submarine cables have composite structural characteristics and work in complex and influential environments, which hinders manual inspection, arouses difficulties in maintenance, and causes challenges on health state assessment. To this end, a data driven and fuzzy synthetic evaluation based submarine cable health state assessment method is proposed in this paper. Firstly, following the technical guidelines of submarine cable health state assessment, dynamic submarine cable online monitoring data and static data including periodic preventive test and operation inspection results are integrated to build a three-layer index system. In addition, long and short-term memory (LSTM) neural network based ultra-short-term prediction models are built to obtain the predictions of online monitoring indices. On this basis, together with the static data, a fuzzy synthetic evaluation method is used to establish a two-stage state evaluation process to obtain the multi-period health states of submarine cables. The effectiveness of the proposed method is verified with an actual submarine cable from an offshore platform in an oilfield of China National Offshore Oil Corporation (CNOOC).

Keywords:

• fuzzy synthetic evaluation
• health state evaluation
• online monitoring data prediction
• submarine cable

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Sichuan Science and Technology Program [No. 2023YFQ0073], the Science project of SWPU [No. 2021JBGS06], and the Scientific Research Starting Project of SWPU [No. 2021QHZ006].

Notes on contributors

Qian Li

Qian Li received the B.S. degree from South China University of Technology, Guangzhou, China, in 2011 and the Ph.D. degree from Sichuan University, Chengdu, China, in 2016. She is currently an associate professor in Southwest Petroleum University School of electrical engineering and information. Her main research interests include power system stability and control, distributed generation and integrated energy systems and electrical equipment condition monitoring.

Jun Wei

Jun Wei received the B.S. degree from Southwest University of Science and Technology, Mianyang, China, in 2021. Now he is a M.Eng. in Southwest Petroleum University. His main research interests include electrical equipment condition monitoring, status assessment and fault diagnosis of electrical equipment.

Chuan Yuan

Chuan Yuan is a senior engineer at State Grid Sichuan Electric Power Company, Chengdu, China. His main research interests include status assessment and fault diagnosis of electrical equipment.

Tianci Su

Tianci Su received the M.S. degree from Southwest Petroleum University, Chengdu, China, in 2022. Now He is now a software engineer at China Electronics Technology Cyber Security Co., Ltd, Chengdu, China. His main research interests include status assessment and fault diagnosis of electrical equipment.

Wei Yang

Wei Yang received the B.S. degree from Sichuan University, Chengdu, China, in 2012 and the Ph.D. degree from Sichuan University, Chengdu, China, in 2018. He is currently an associate professor in Southwest Petroleum University School of electrical engineering and information. His main research interests are integrated energy systems and comprehensive utilization of natural gas pressure energy.

Anan Zhang

Anan Zhang received the B.S. degree from Sichuan University, Chengdu, China, in 2000 and the Ph.D. degree from Sichuan University, Chengdu, China, in 2010. He is currently a professor in Southwest Petroleum University School of electrical engineering and information. His main research interests include integrated energy systems, electric - gas coordination control and electrical equipment condition monitoring.