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Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

Subsea power cables are essential assets for the electrical transmission and distribution networks. They are crucial in ensuring the security of electricity supply and supporting the global expansion in offshore renewable energy generation. After reviewing historical data on subsea cable failure modes, we established that existing monitoring systems do not account for over 70% of subsea cable failure modes. The current technologies focus on electrical failure modes and subsea cable asset management strategies are typically reactive or time based, with inspection limited to diver and/or ROV supported video footage which has several limitations, such as requiring good visibility, access to the cable, challenges in locating the cable and inability to identify failure modes at the interface of the seabed. To overcome these limitations, we propose an innovative sensor technology that can provide the in-situ integrity analysis of the subsea cable. In this paper, we applied low frequency sonar technology to undertake detailed and in-situ assessment of subsea cable integrity. Specifically, in our work, a wideband low frequency (LF) sonar scanning system is manufactured to collect acoustic response from different subsea power cable samples with different inner structure and external failure modes. In addition, accelerated life cycle testing was conducted by manually introduce controlled stages of corrosion and abrasion to the cables to obtain integrity data at various cable degradation levels. Seminal results provide a detailed library of LF sonar responses to cable type and failure mode variations. The results of preliminary data analysis demonstrate the ability to distinguish subsea cables by differences in diameter and cable types and achieve an overall 95%+ accuracy rate to detect different cable degradation stages.

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.