Subsea power cables are critical infrastructure for the continuity of energy supply and are a key enabler to the global growth in offshore renewable energy generation. Capital projects for long range, greater than 60km distances, for transmission networks can cost in excess of £1
...
Subsea power cables are critical infrastructure for the continuity of energy supply and are a key enabler to the global growth in offshore renewable energy generation. Capital projects for long range, greater than 60km distances, for transmission networks can cost in excess of £1billion. In this paper, we have extensively reviewed the data within academia and industry with respect to the practices and challenges of subsea power cable management. With a detailed focus on 15 years of historical cable failure data from the UKs largest owner of subsea power cables, we identified that existing commercial monitoring systems do not monitor about 70% of subsea power cable failure modes. To overcome the challenges this represents to delivering cost effective and timely intervention to subsea power cables, we present a fusion prognostic model to enable predictive forecast on cable failure modes, include location and rates of degradation. In our model, we incorporate physical models to simulate the process where common cable failure modes lead to cable damage, such as abrasion and corrosion. In addition, we implemented multi-physics modelling techniques to model cable displacement and scouring, taking into consideration different environmental condition profiles. We also demonstrate how new sensing technologies can be integrated into this sensor agnostic model in order to enhance lifetime prediction accuracy. An operational decision support system is implemented within this work to integrate these different physical failure models, using a fusion model approach which integrates in-situ inspection data from sonar, autonomous underwater vehicle (AUV) inspection mission planning, and data analysis results into a holistic subsea cable remaining useful life prediction capability.
@en