D2 - Smart sensor system use
D - Sensing, Control and Electromechanics
Improved control and operations requires better use of sensor systems to measure of behaviour of individual ORE devices and arrays and the environments in which they operate.
Identify, evaluate and validate sensor technologies, data transmission, integration and interpretation systems to support improved control and management.
Context And Need
Although ORE devices, particularly wind turbines, have a large number of sensors measuring individual device performance, the measurements are typically not treated in an integrated manner to allow rich understanding to be extracted. In addition, device measurements are typically not linked to comprehensive environmental measurements. As ORE devices become larger and more complex additional sensing modalities will be required to support improved control and structural health monitoring.
Smart sensors may be embedded within structures and ORE machines, or may be developed as part of autonomous monitoring systems. In order to develop sensors fit for purpose in the offshore ORE environment, there is a need to identify, evaluate and validate sensor technology, data transmission, integration and interpretation systems to support control and planning of operations and maintenance. This includes both sensing applied to individual ORE devices, arrays of devices and the environments in which they operate.
Improved understanding of ORE device behaviour, along with the environmental drivers (wind, waves and tides) can help reduce LCoE through improved energy yield, improved prediction of remaining lifetime, and improved planning of maintenance operations and reduction of damage from extreme events. Improved planning of O&M can also reduce the need for high-risk offshore operations. Improved measurement of environmental impacts can help reduce unwanted impacts and improve societal acceptance of ORE.
Current activity includes EPSRC funded projects:
- New Partnership in Offshore Wind (EP/R004900/1), 2017-2022
- Structural Health Monitoring of Systems of Systems: Populations, Networks and Communities, (EP/R003645/1), 2018-2021
- HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms, (EP/P009743/1), 2017-2020
- Condition Monitoring of Wind Turbine Drive-Trains via Non-Contact Acoustic Sensors, 2016-2019
Supergen ORE Hub - Flexible Funding Research
- V-SCORES (Validating Surface Currents at Offshore Renewable Energy Sites)
Lead Institution: University of the Highlands and Islands
Marine current measurements are vital for tidal resource estimation and resilient design criteria for all Offshore Renewable Energies (ORE). In-situ measurements are costly, and retrieval of seabed mounted equipment is not guaranteed. Moreover, in many potential ORE locations globally, suitable field survey campaigns may not be viable. Until now, most data for model validation and impact assessment have focused on temporal variability from single-point measurements, yet spatial variability is of critical importance. Additionally, most oceanographic current measurements are sub-surface; the near-surface zone is largely unknown due to instrument limitations (e.g., surface interference making the top few “bins” of ADCP data unusable). The development of low-cost and low-risk surface current mapping tools, and translating this knowledge to flow at depth, is therefore a key challenge in ORE development. Surface current maps would provide high-resolution detail needed to measure spatial heterogeneity, understand realworld wakes and the relationship between flow and animal behaviour when combined with ecological surveys. A better understanding of surface currents will also improve resilience of floating ORE and yield of floating tidal turbines. The aim of V-SCORES is comprehensive validation of unmanned aerial vehicle (UAV) techniques for surface current spatial mapping, demonstrated at tidal stream sites. Field campaigns will be conducted at contrasting commercial sites (Pentland Firth, Scotland & Ramsey Sound, Wales) under different environmental conditions (wave exposure, operational turbines installed, etc.).
- Smart piezoelectric metamaterials for partial discharge monitoring
Lead Institution: University of Strathclyde
The proposed research will improve the reliability and availability of offshore electrical infrastructure components via 3D printed, smart acoustic sensors which can be tailored to specific cable and junction properties and are robust in challenging environments. This proposal comprises the design and manufacture of a perovskite structured piezoelectric with an anisotropic response tailored to a cable termination and evaluation of its performance in terms of sensitivity gain and reliability of detection of partial discharges. The proposal comprises two streams of work which may be parallelized to some extent. Work under the first stream will examine the design space of the perovskite structured sensors in terms of modelling and experimentation to develop a proof-of-principle sensor. This sensor will be evaluated in terms of the sensitivity and signal to noise ratio in laboratory settings. The second work stream investigates an acoustic emission detection system, and the embedding of a tailored 3D printed sensor in a cable termination. This second stream will be carried out through an additional 6 months’ support from our Electrical Infrastructures Research Hub (EIRH) collaboration with ORE Catapult (OREC).
- A hybrid and scalable digital twin for intelligent direct drive powertrain condition monitoring
Lead Institution: University of Strathclyde
As larger wind turbines with newer powertrain technologies are introduced in the offshore wind sector, state-of-the-art machine learning techniques that use past field data are no longer directly applicable. Operational alarms based on physical models of older turbines are often no longer valid with new powertrain technology. This represents a key vulnerability in the offshore wind sector. This project will develop a hybrid digital twin combining transfer learning and physical modelling approaches that will be able to model normal and abnormal behaviour for new turbines before operational data is available. As turbines move further offshore, operators are motivated to reduce the number of turbine visits for cost and safety reasons. The hybrid models proposed in this application could be used to reduce the number of powertrain inspection and service visits. The requirement for visits will be reduced through the digital twin providing additional health indicators and recommendations to the operators, and by adding confidence to the use of existing health indicators provided by SCADA and monitoring systems.
Other active research projects:
- FLOTANT (Innovative, low cost, low weight and safe floating wind technology optimized for deep water wind sites): The main objective of FLOTANT is the development of innovative solutions to improve the robustness and cost-efficiency of 10+MW wind turbine generators in deep waters (100-600m). This goal will be achieved through the design and test of specific components, as well as the assessment and optimisation of the construction, installation, operation and decommissioning techniques, in line with state-of-the-art practices and environmental constraints.
We would also like to invite UK researchers and industry stakeholders within ORE to submit links to research projects, both past and present, for inclusion within the landscape.
Therefore, if you have a UK-based research project within an area of ORE that you feel is relevant to a specific research theme or challenge within the Research Landscape, click HERE to submit your research project to the research landscape
PhD projects in Offshore Renewable Energy
In order to better understand the breadth of ORE research currently being conducted in the UK, the Supergen ORE Hub has collated from its academic network, UK Centres for Doctoral Training and Industrial partners, a list of PhDs currently being undertaken in ORE.