Subsea Power Cable Health Management Using Machine Learning Analysis of Low-Frequency Wide-Band Sonar Data

Wenshuo Tang, Keith Brown, Daniel Mitchell, Jamie Blanche, David Flynn

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
19 Downloads (Pure)

Abstract

Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables.
Original languageEnglish
Article number6172
JournalEnergies
Volume16
Issue number17
Early online date25 Aug 2023
DOIs
Publication statusPublished - 1 Sept 2023

Keywords

  • health management
  • condition monitoring
  • sensing
  • machine learning
  • asset integrity
  • low-frequency sonar
  • subsea power cable

ASJC Scopus subject areas

  • Control and Optimization
  • Energy (miscellaneous)
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment

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