Wind energy is expected to be a major contributor to the global energy supply in the coming decades, and in many areas it is already the cheapest form of electricity generation available. Offshore wind energy is now playing a significant and rapidly increasing role in areas with suitable shallow seas. A further step to floating offshore wind turbines, which are just beginning to be installed commercially, could massively increase the potential offshore wind resource by allowing deeper sea areas to be used.
Research over many years has led to the very large, efficient and cost-effective wind turbine designs seen in today's wind farms. Much recent research has focussed on more efficient and cost-effective installation and operation of wind farms. There is serious interest in novel wind farm control strategies which can improve the operation of the wind farm as a whole, rather than just controlling each wind turbine as if it was operating in isolation from its neighbours. One strategy for example, known as 'wake steering', attempts to deflect each turbine's wake away from downstream turbines, allowing increased overall power production, and longer lifetime through reduced fatigue damage.
This study will focus on the use of wake steering in a floating offshore wind farm context. More research is required to get a deeper understanding of the wake effects, particularly in wind conditions typical of offshore environments, so that the most effective control strategies can be devised.
_DNV GL, Durham University_ and _Ocean Flow Energy_ have partnered to apply to this R&D programme in order to investigate the feasibility of this innovative control technology on floating-offshore wind farms. The main objectives of this research proposal include:
* increasing confidence in the use of wake steering,
* identifying technical challenges and advantages of using wake steering on floating offshore wind farms,
* analysing the effects of the use of wake steering using the Starfloat floating platform design as a benchmark,
* analysing the effects of the application of wake steering on the economic performance of a floating offshore wind farm.
This research will be carried out in cooperation with a US consortium, led by NREL, which has been formed to investigate similar control strategies for fixed offshore wind farms. Sharing of expertise and wind farm data will lead to improved wake modelling techniques which will help bring the technical and economic benefits of wake steering to the growing US and UK offshore wind farm markets.
Visual inspection of large infrastructure assets in extreme environments, such as offshore wind turbines, is essential for maintaining performance and ensuring safety. However, it is expensive, labour intensive, and hazardous, with the majority of inspections being carried out manually by inspectors using rope access or boom lift. Currently tens of millions of man hours per annum are spent looking for defects in safety critical infrastructure.
Automatic visual inspections using robots equipped with cameras offers a potential solution, significantly reducing costs, increasing quality assurance and reducing safety concerns. Major progress has been made in capturing images for inspection, notably using drones, and in some cases, these have advanced to the point of being technically viable alternatives to traditional manual inspections.
However, although the capture of images has been automated, the processing of images in order to identify defects remains a semi-automated process, with reliance on visual inspections of image data by trained experts. This means that although the visual data can be collected automatically and quickly, inspections still take a significant amount of time. Hence there is a need for fully automating the data processing component if these systems are to fully realise their potential.
Alongside this there is also a need to garner wider acceptance of automatic processing techniques across the industry. They need to be commercially viable and demonstrably reliable - adoption of new approaches to inspection relies on a full understanding of their limitations and the criteria by which they can be assessed and categorised. This is necessary if such methods are to be acceptable to current and future regulatory requirements. Addressing this issue is therefore of equal importance to technical development.This project aims to address both of these issues. It is a collaboration between Garrad Hassan (DNV-GL), world leaders in risk management and quality assurance, Perceptual Robotics (PR), an SME specialising in visual inspection of wind turbines using drones, and the University of Bristol (UoB), experts in computer vision and AI. An automated processing pipeline will be developed and demonstrated, and incorporated within PRs Dhalian system, a world leading semi-automated drone inspection system for wind turbines. Alongside this, a general framework for assessing, characterising and comparing such systems will be validated and verified, with the aim of generating broader acceptance across the industry and informing future regulation. The project will provide both competitive advantage to PR and contribute to growth of the UK automated inspection industry.
Converting sea wave motion into electrical energy is challenging, in part due to the relatively low speeds and irregular movements of ocean waves. WITT Ltd (WL) has devised a scalable technological innovation in energy generation called the WITT that has the capability to harness wave energy. The innovation concerns a patented transmission system that converts mechanical motion in the full six degrees into a single unidirectional rotation used to drive an efficient generator for the production of electricity. No other transmission is capable of collecting all of this chaotic motion and turning it into useable power. Sealed unit operation is potentially maintenance free and suitable for marine environments. It can also be built from tried and tested industry components. For this reason the WITT potentially provides a compelling marine energy harvesting device. This feasibility project will assess the capability and potential of the WITT to be deployed offshore as a wave energy converter to generate power compared to competitive wave energy converter technologies. It will determine whether it does indeed potentially offer a standout solution capable of UK wide scale deployment.
The project explores the potential to lower the cost and improve the efficiency of wind power generation through actively controlled, textile-covered blades. These blades have the potential to push the boundaries of current technology, improving the design, manufacture, assembly and maintenance of the blades.
Enginered Textile blades with load active control can make a major contribution to solving the UK's energy trilemma. By reducing the cost of production, transportation and installation of the blades, and increasing the blade efficiency it will help to drive the further deployment of on- and offshore wind turbines.
The project team - consisting of SMAR Azure Ltd, DNV-GL and ORE Catapult - has the required technical skills and engineering tools, commercial expertise and management experience to ensure that the project is delivered successfully.
SWEPT2 follows the successful “Simulated Wake Effects Platform for Turbines” project to establish the viability of GPU-based fluid dynamics simulation of turbine array wake interaction effects. With a view to growing a UK-based technology supply chain, the original partners (DNV GL, Zenotech and the University of Bristol) will be joined by the Offshore Renewable Energy Catapult (providing access to LIDAR data for validation), STFC Daresbury (to apply the latest in big-data analytics to the challenge of comparing CFD & experimental data), CFMS (cloud computing integration and optimisation), and the universities of Surrey, Strathclyde and Imperial College – to provide expertise in wake turbulence and wind tunnel data. SSE has agreed to independently assess the functionality and value of the service during the project. SWEPT2 addresses the energy trilemma by (i) reducing costs, thus enabling a displacement of fossil fuels thereby (ii) cutting carbon emissions & (iii) reducing dependence on insecure imports.
GL Garrad Hassan (GH) has a long track record in designing structural components for large wind turbines (eg nacelle machine frame, bearing housing, hub). The designs have evolved over several years, but can be optimised further. GH believes that the method of numeric topology optimisation may be an alternative option to further optimise structural components. Reducing tower head mass also leads to reduced tower and support structure weight. GH plans to evaluate Topology optimisation software (Tosca) on an existing 7MW offshore wind turbine design. Based on the layout of this turbine an 'optimised' nacelle machine frame and bearing housing(s) are generated using numeric topology optimisation. The 'traditional' design and 'optimised' will be compared. Relevant criteria are: fatigue and ultimate strength, but also load distribution on for instance the yaw bearing will be evaluated.
GL Garrad Hassan is conducting fully integrated offshore wind turbine and support structure design with an objective of demonstrating the potential savings in lifetime cost of energy though better optimisation of the support structure. The supply and installation of an offshore wind turbine support structure accounts for about 28% of the lifetime cost of energy. The integrated design approach aims to reduce conservatism in the support structure design by correctly capturing the global dynamics and combined wind and wave loading of the system. Furthermore, it provides an opportunity for intelligent turbine control system action aimed specifically at minimising loads in the support structure, and hence further reducing steel mass and cost. GH will conduct all the work, no external partners are involved. GH is confident of demonstrating potential cost of energy savings; this should benefit offshore wind in general and support the growth of the UK supply chain for offshore wind.