The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

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In this paper, we present two novel sensor systems to support the integrity analysis and asset management of subsea power cables. Firstly, we provide an example of a customized in-situ monitoring collar for subsea power cable monitoring, representing a first in full displacement monitoring of subsea power cables. Secondly, we provide results from an advanced Low Frequency (LF) sonar system, demonstrating the technology's ability to differentiate different power cable types and varying levels of degradation. Results from laboratory experiments verify the ability of the monitoring collar to wirelessly monitor cable dynamics and an accuracy of 95% from LF Sonar analysis utilizing the state-of-the-art deep learning method (Convolution Neural Network) for detecting different level of cable damage. Our results provide a new ability to perform in-situ cross sectional analysis of subsea cables, and our monitoring collar concept can provide new information into real-time cable dynamics.

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Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion.

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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.

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In future inspections of offshore assets utilizing robots, robots will not only be expected to collate new data from their payload of instruments, but they will also be expected to interact with the infrastructure being inspected, undertake remedial tasks and engage with embedded monitoring systems of the asset. This increasing level of interaction and deployment frequency of robot inspections requires an understanding of how we can embed safe and trusted operational architectures within robots. Currently, robots can undertake constrained semi-autonomous inspections, using predetermined tasks (missions) with minimum supervision. However, the challenge is that the state of the world changes with time as does the condition of the robot. Therefore, robots must be able to undertake adaptive measures to support optimal outcomes during autonomous missions. In this paper, we propose an initial architecture to the safe verification and validation of health condition and certification of robotic and autonomous inspection systems for offshore assets. Our first contribution relates to the verification and validation architecture, which takes into account risks associated with asset inspection, safety protocols, evolving ambient changes, as well as the inherent state of health of the robot. The second part of our paper looks to how prognostic analytics can be used to support robot resilience in terms of sensor drift and accurate state of health estimates of critical sub-systems. Initial results demonstrate that methods such as relevance vector machines and Bayesian networks can be used to accurately mitigate risks to autonomy.

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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.

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In this paper we present a review into data driven prognostics and its relevance to resilience in energy systems. A data driven remaining useful life prediction for Li-ion batteries utilizing data analysis via a relevance vector machine (RVM) model is shown to be within 5% accuracy when applied to large lifecycle datasets. Results demonstrate that due to the agile nature of prognostic models and their accuracy, prognostics and health management methods will be vital to resilient and sustainable energy systems.

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Battery energy storage systems can assist distribution network operators (DNOs) to face the challenges raised by the substantial increase in distributed renewable generation. A challenge is that these resources are intermittent and often 'invisible'to the DNO. If not monitored, the aggregate size of small embedded generation resources can cause thermal wearing of distribution assets and voltage excursions, especially in sunny/windy periods with insufficient local demand. Several developers of energy storage solutions, with technologies such as lithium-ion (Li-ion) batteries, offer their products to address peak shaving, frequency and voltage control needs within the network. Once deployed within the energy network batteries experience capacity degradation with usage, these companies will need to incorporate methods from prognostics and health management (PHM) in order to better manage their products. The main deliverable of this project is validation of data analysis, based on relevance vector machine, to predict the remaining useful life of Li-ion batteries. The accuracy of the predictions for different batteries is all within 10 cycles (within 8.5% relative error). These results confirm the importance of PHM methods within a distribution system operator model, where lifecycle management of critical sub-systems and systems will become increasingly important to network operators.@en

In recent years there has been a surge of interest in hybrid propulsion technology for commercial and domestic maritime vessels. Factors driving this growth include high maintenance cost of diesel engines and the need for compliance to more stringent environmental regulations. Although electric and hybrid propulsion techniques present an opportunity to address this challenge, today's batteries for large vessels are still expensive and prone to unexpected failure. Therefore, the cost and reliability of batteries still represent risks to the maintenance in hybrid energy systems. In this paper, we first introduce the challenges of asset health management within hybrid energy systems for maritime vessels. We explore the feasibility of data-driven methods to evaluate Remaining Useful Life (RUL) of the system assets to inform a preventative and cost efficient management system. An overview of our proposed solution is presented along with preliminary results of our data-driven model applied to large battery data sets to estimate the battery RUL. The model is based on a state-of-art machine learning technique, Relevance Vector Machine (RVM), which is a powerful tool for resolving uncertainty in large data sets. Initial training of our machine learning algorithm utilizes a public battery life cycle testing dataset, provided from NASA Ames Research Center. Next, we use life cycle analysis of batteries designed for hybrid vessels to evaluate the performance of the algorithm in predicting battery remaining useful life (RUL). The accuracy of the predictions for different batteries are all within 10 cycles (within 8.5% relative error) which encourages us to adopt the same approach in future health management works for other power assets on the hybrid maritime vessels.@en