Bailey, Christopher ORCID: https://orcid.org/0000-0002-9438-3879, Yin, Chunyan ORCID: https://orcid.org/0000-0003-0298-0420 and Rajaguru, Pushparajah ORCID: https://orcid.org/0000-0002-6041-0517 (2019) Predicting damage and life expectancy of subsea power cables in offshore renewable energy applications. IEEE ACCESS, 7. pp. 54658-54669. ISSN 2169-3536 (Online) (doi:10.1109/ACCESS.2019.2911260)

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Abstract

Subsea power cables are critical assets within the distribution and transmission infrastructure of electrical networks. Over the past two decades, the size of investments in subsea power cable installation projects has been growing significantly. However, the analysis of historical failure data shows that the present state-of-the-art monitoring technologies do not detect about 70% of the failure modes in subsea power cables. This paper presents a modelling methodology for predicting damage along the length of a subsea cables due to environmental conditions (e.g. seabed roughness and tidal flows) which result in loss of the protective layers on the cable due to corrosion and abrasion (accounting for over 40% of subsea cable failures). For a defined cable layout on different seabed conditions and tidal current inputs, the model calculates cable movement by taking into account the scouring effect and then it predicts the rate at which material is lost due to corrosion and abrasion. Our approach integrates accelerated aging data using a Taber test which provides abrasion wear coefficients for cable materials. The models have been embedded into a software tool that predicts the life expectancy of the cable and demonstrated for narrow conditions where the tidal flow is unidirectional and perpendicular to the power cable. The paper also provides discussion on how the developed models can be used with other condition monitoring data sets in a prognostics framework.

Item Type:	Article
Uncontrolled Keywords:	Offshore renewable energy; Subsea cables; degradation; prognostics; life expectancy; abrasion; wear; corrosion; scour
Subjects:	Q Science > QA Mathematics
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:06
URI:	http://gala.gre.ac.uk/id/eprint/23448

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