Optimal Sensor Placement

Published 8 September 2022

Current monitoring systems on wind turbines are hugely varied and relatively limited. The offshore wind industry also has some knowledge gaps around sensor criticality, the root causes of major failures, sensor data inter-correlation and affordable scalable sensor solutions.

The Solution

Transmission Dynamics, an award-winning engineering company who specialise in design, development and deployment of wireless industrial sensor solutions and  Unasys, experts in developing digital models, have joined forces with ORE Catapult and the University of Strathclyde to form a UK consortium.

The consortium has been working in collaboration with a US team, led by Tufts University, whose project, Optimal Sensor Placement for Physics-Based Digital Twins, began in January 2021.

The bilateral collaboration is a unique arrangement where deliverables are shared between the UK and US teams, and it is hoped will pave the way in driving down the cost of operating and maintaining offshore windfarms across the world.

The Project

The project is focussed on identifying optimal sensor placement for digital twinning technology to enable informed and optimised O&M planning and elimination of unnecessary precautionary inspections and interventions for the global offshore wind industry.

OSP is doing this by developing a state-of-the-art, holistic monitoring system to collect, analyse, and interpret data acquired from an offshore wind turbine.

Sensors placed on wind turbines provide data that can improve the working capability of a turbine. The project is investigating where current sensors are adding value and identifying the best type and placement of sensors to capture the data needed to monitor a wind turbine’s entire lifecycle.

A digital twin model is being developed by creating a digital replica of the physical wind turbine. Data retrieved from the physical wind turbine passes seamlessly to a simultaneously existing virtual twin. A visual digital twin of ORE Catapult’s Levenmouth Demonstration Turbine (LDT) is being developed by Unasys, and ongoing monitoring of the LDT system will be possible through data driven digital twin analytics developed by ORE Catapult. This brings together information on critical turbine components such as pitch bearings, the power converter, and the turbine tower. The Yaw, pitch bearing, blade pitch angle and the rotation speed are all simulated in the digital twin.

ORE Catapult and Transmission Dynamics are also developing physics based digital twins, capitalising on additional sensors designed and installed by Transmission Dynamics on ORE Catapult’s Levenmouth Demonstration Turbine. A methodology to quantify measurement uncertainties and fatigue damage progression modelling is being developed by Strathclyde University to enhance development of these physics based digital twins.

Testing and research can be carried out on the digital twin rather than on a real wind turbine, transforming future turbine operations and maintenance, increasing safety and extending the turbine life. Digital twin analytics are smart, adaptable, and capable of remote damage detection.

ORE Catapult and Transmission Dynamics are collaboratively reviewing traditional supervisory control and acquisition (SCADA) data currently available from sensors on the Levenmouth Turbine. The project also examines any gaps in the information retrieved and will highlight areas of improvement for the development of wind industry digital twins.

The project directly addresses challenges around improving turbine operation and maintenance – reducing the need for technicians to go offshore, maximising the availability of offshore wind turbines, and ultimately reducing the levelised cost of energy (LCOE).

The consortium is currently scoping and reviewing funding opportunities to continue further work in this area.


If you’re a turbine OEM, a component manufacturer, or a wind farm operator, reach out to our team to see how you can apply this in real life to improve your offering.

Contact Our Team

Dr Ampea Boateng

Senior Research Engineer, Intelligence Condition Monitoring

Email Dr Ampea Boateng

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