IEEE Access, Vol. 7, No. 1, pp. 54658-54669, 2019, DOI:10.1109/ACCESS.2019.2911260

Predicting Damage and Life Expectancy of Subsea Power Cables in Offshore Renewable Energy Applications

Fateme Dinmohammadi 1, David Flynn 1, Chris Bailey 2, Michael Pecht 3, Chunyan Yin 2, Pushpa Rajaguru 2, and Valentin Robu 1
1 Smart Systems Group, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK, EH14 4AS
2 Department of Mathematical Sciences, University of Greenwich, Greenwich, London, UK, SE10 9LS
3 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, USA


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.

This article is available online here.

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